Table of Contents

• 1. Initiation
  • 1.1 Loading of all libraries
  • 1.2 Exploring the data
  • 1.3 Missing Values
  • 1.4 Data enrichment
• 2. Data Analysis
  • 2.1 Top Goalscorers
  • 2.2 Top Assisters
  • 2.3 Contribution for Goal
  • 2.4 Time needed to score a goal
  • 2.5 Time needed to assist
  • 2.6 Top 20 Players by Total Goal Contribution per minute (Goals + Assists)
  • 2.7 Players with most yellow cards
  • 2.8 Players with most red cards
  • 2.9 Players with most cards (Yellow / Red)
• 3. Contribution per year of the 3 best players (Goals / Assists)
• 4. Sporting Clube de Portugal
  • 4.1 Average stadium assistance 2012-2023
  • 4.2 Players Market Value 2023
• 5. Conclusion

This dataset was downloaded on Kaggle and it contains football player data scraped from Transfermarkt, that is updated weekly. It includes information on over 60,000 games played across multiple seasons and major competitions, with details on over 400 clubs and 30,000 players.

The objective of this study, in the first instance is to make some statistical analyses to understand:

• Who are the 20 players that scored more goals

• Who are the 20 players that assisted more times

• Who has more contribution to goals

• Who are the 20 players that need less time to score a goal

• Who are the 20 players that need less time to assist someone to score

• Top 20 players that got more yellow cards

• Top 20 players that got more red cards

• Top 20 players with more card combination.

In a second instance I'm going to analyze Sporting Clube de Portugal. For some people that don't know much about this club, it's the club where golden ball winners Cristiano Ronaldo and Luis Figo started to play, it was also the first professional club for so many players as Nani, Quaresma, Joao Moutinho , Rafael Leao etc.

1. Initiation

1.1 Loading of all libraries

import pandas as pd
import matplotlib.pyplot as plt
import numpy as  np
from scipy import stats as st
import seaborn as sns
import plotly.express as px

1.2 Exploring the data

presencas = pd.read_csv('/Users/andrewferreira/Downloads/archive (1)/appearances.csv')
presencas.head()

	appearance_id	game_id	player_id	player_club_id	player_current_club_id	date	player_name	competition_id	yellow_cards	red_cards	goals	assists	minutes_played
0	2231978_38004	2231978	38004	853	235	2012-07-03	Aurélien Joachim	CLQ	0	0	2	0	90
1	2233748_79232	2233748	79232	8841	2698	2012-07-05	Ruslan Abyshov	ELQ	0	0	0	0	90
2	2234413_42792	2234413	42792	6251	465	2012-07-05	Sander Puri	ELQ	0	0	0	0	45
3	2234418_73333	2234418	73333	1274	6646	2012-07-05	Vegar Hedenstad	ELQ	0	0	0	0	90
4	2234421_122011	2234421	122011	195	3008	2012-07-05	Markus Henriksen	ELQ	0	0	0	1	90

presencas['competition_id'].unique()

array(['CLQ', 'ELQ', 'UKRS', 'RUP', 'UKR1', 'DK1', 'RUSS', 'RU1', 'BESC',
       'UKRP', 'BE1', 'FRCH', 'POCP', 'SC1', 'NLSC', 'CIT', 'FR1', 'NL1',
       'SCI', 'POSU', 'DFL', 'GBCS', 'DFB', 'TR1', 'PO1', 'GB1', 'ES1',
       'SUC', 'L1', 'GR1', 'IT1', 'CDR', 'DKP', 'USC', 'CL', 'EL', 'NLP',
       'SFA', 'GRP', 'FAC', 'KLUB', 'ECLQ', 'CGB'], dtype=object)

presencas.info(memory_usage='deep')

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1524126 entries, 0 to 1524125
Data columns (total 13 columns):
 #   Column                  Non-Null Count    Dtype 
---  ------                  --------------    ----- 
 0   appearance_id           1524126 non-null  object
 1   game_id                 1524126 non-null  int64 
 2   player_id               1524126 non-null  int64 
 3   player_club_id          1524126 non-null  int64 
 4   player_current_club_id  1524126 non-null  int64 
 5   date                    1524126 non-null  object
 6   player_name             1524125 non-null  object
 7   competition_id          1524126 non-null  object
 8   yellow_cards            1524126 non-null  int64 
 9   red_cards               1524126 non-null  int64 
 10  goals                   1524126 non-null  int64 
 11  assists                 1524126 non-null  int64 
 12  minutes_played          1524126 non-null  int64 
dtypes: int64(9), object(4)
memory usage: 500.6 MB

presencas.info(memory_usage='deep')

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1524126 entries, 0 to 1524125
Data columns (total 13 columns):
 #   Column                  Non-Null Count    Dtype 
---  ------                  --------------    ----- 
 0   appearance_id           1524126 non-null  object
 1   game_id                 1524126 non-null  int64 
 2   player_id               1524126 non-null  int64 
 3   player_club_id          1524126 non-null  int64 
 4   player_current_club_id  1524126 non-null  int64 
 5   date                    1524126 non-null  object
 6   player_name             1524125 non-null  object
 7   competition_id          1524126 non-null  object
 8   yellow_cards            1524126 non-null  int64 
 9   red_cards               1524126 non-null  int64 
 10  goals                   1524126 non-null  int64 
 11  assists                 1524126 non-null  int64 
 12  minutes_played          1524126 non-null  int64 
dtypes: int64(9), object(4)
memory usage: 500.6 MB

clube_jogos = pd.read_csv('/Users/andrewferreira/Downloads/archive (1)/club_games.csv')
clube_jogos.head()

	game_id	club_id	own_goals	own_position	own_manager_name	opponent_id	opponent_goals	opponent_position	opponent_manager_name	hosting	is_win
0	2320450	1468	0	NaN	Holger Bachthaler	24	2	NaN	Armin Veh	Home	0
1	2320460	1	3	NaN	Jürgen Luginger	86	1	NaN	Robin Dutt	Home	1
2	2320472	2036	4	NaN	Frank Schmidt	72	5	NaN	Alexander Schmidt	Home	0
3	2321044	16	2	1.0	Jürgen Klopp	23	1	15.0	Torsten Lieberknecht	Home	1
4	2321060	23	0	18.0	Torsten Lieberknecht	24	2	11.0	Armin Veh	Home	0

clube_jogos.info(memory_usage='deep')

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 132330 entries, 0 to 132329
Data columns (total 11 columns):
 #   Column                 Non-Null Count   Dtype  
---  ------                 --------------   -----  
 0   game_id                132330 non-null  int64  
 1   club_id                132330 non-null  int64  
 2   own_goals              132330 non-null  int64  
 3   own_position           92890 non-null   float64
 4   own_manager_name       130842 non-null  object 
 5   opponent_id            132330 non-null  int64  
 6   opponent_goals         132330 non-null  int64  
 7   opponent_position      92890 non-null   float64
 8   opponent_manager_name  130842 non-null  object 
 9   hosting                132330 non-null  object 
 10  is_win                 132330 non-null  int64  
dtypes: float64(2), int64(6), object(3)
memory usage: 34.5 MB

clubes = pd.read_csv('/Users/andrewferreira/Downloads/archive (1)/clubs.csv')
clubes.head()

	club_id	club_code	name	domestic_competition_id	total_market_value	squad_size	average_age	foreigners_number	foreigners_percentage	national_team_players	stadium_name	stadium_seats	net_transfer_record	coach_name	last_season	filename	url
0	105	sv-darmstadt-98	Sportverein Darmstadt 1898 e. V.	L1	NaN	30	26.7	11	36.7	1	Merck-Stadion am Böllenfalltor	17810	€-1.48m	NaN	2023	../data/raw/transfermarkt-scraper/2023/clubs.j...	https://www.transfermarkt.co.uk/sv-darmstadt-9...
1	11127	ural-ekaterinburg	FK Ural Yekaterinburg	RU1	NaN	27	27.9	15	55.6	6	Yekaterinburg Arena	23000	€-770k	NaN	2023	../data/raw/transfermarkt-scraper/2023/clubs.j...	https://www.transfermarkt.co.uk/ural-ekaterinb...
2	114	besiktas-istanbul	Beşiktaş Jimnastik Kulübü	TR1	NaN	30	27.7	14	46.7	12	Tüpraş Stadyumu	42590	€-14.50m	NaN	2023	../data/raw/transfermarkt-scraper/2023/clubs.j...	https://www.transfermarkt.co.uk/besiktas-istan...
3	12	as-rom	Associazione Sportiva Roma	IT1	NaN	27	27.2	18	66.7	17	Olimpico di Roma	73261	+€64.15m	NaN	2023	../data/raw/transfermarkt-scraper/2023/clubs.j...	https://www.transfermarkt.co.uk/as-rom/startse...
4	148	tottenham-hotspur	Tottenham Hotspur Football Club	GB1	NaN	28	26.0	22	78.6	21	Tottenham Hotspur Stadium	62850	€-154.90m	NaN	2023	../data/raw/transfermarkt-scraper/2023/clubs.j...	https://www.transfermarkt.co.uk/tottenham-hots...

clubes.info(memory_usage='deep')

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 426 entries, 0 to 425
Data columns (total 17 columns):
 #   Column                   Non-Null Count  Dtype  
---  ------                   --------------  -----  
 0   club_id                  426 non-null    int64  
 1   club_code                426 non-null    object 
 2   name                     426 non-null    object 
 3   domestic_competition_id  426 non-null    object 
 4   total_market_value       0 non-null      float64
 5   squad_size               426 non-null    int64  
 6   average_age              388 non-null    float64
 7   foreigners_number        426 non-null    int64  
 8   foreigners_percentage    378 non-null    float64
 9   national_team_players    426 non-null    int64  
 10  stadium_name             426 non-null    object 
 11  stadium_seats            426 non-null    int64  
 12  net_transfer_record      426 non-null    object 
 13  coach_name               0 non-null      float64
 14  last_season              426 non-null    int64  
 15  filename                 426 non-null    object 
 16  url                      426 non-null    object 
dtypes: float64(4), int64(6), object(7)
memory usage: 289.5 KB

competicoes = pd.read_csv('/Users/andrewferreira/Downloads/archive (1)/competitions.csv')
competicoes.head()

	competition_id	competition_code	name	sub_type	type	country_id	country_name	domestic_league_code	confederation	url
0	CIT	italy-cup	italy-cup	domestic_cup	domestic_cup	75	Italy	IT1	europa	https://www.transfermarkt.co.uk/italy-cup/star...
1	NLSC	johan-cruijff-schaal	johan-cruijff-schaal	domestic_super_cup	other	122	Netherlands	NL1	europa	https://www.transfermarkt.co.uk/johan-cruijff-...
2	GRP	kypello-elladas	kypello-elladas	domestic_cup	domestic_cup	56	Greece	GR1	europa	https://www.transfermarkt.co.uk/kypello-ellada...
3	POSU	supertaca-candido-de-oliveira	supertaca-candido-de-oliveira	domestic_super_cup	other	136	Portugal	PO1	europa	https://www.transfermarkt.co.uk/supertaca-cand...
4	RUSS	russian-super-cup	russian-super-cup	domestic_super_cup	other	141	Russia	RU1	europa	https://www.transfermarkt.co.uk/russian-super-...

competicoes.info(memory_usage='deep')

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 43 entries, 0 to 42
Data columns (total 10 columns):
 #   Column                Non-Null Count  Dtype 
---  ------                --------------  ----- 
 0   competition_id        43 non-null     object
 1   competition_code      43 non-null     object
 2   name                  43 non-null     object
 3   sub_type              43 non-null     object
 4   type                  43 non-null     object
 5   country_id            43 non-null     int64 
 6   country_name          36 non-null     object
 7   domestic_league_code  36 non-null     object
 8   confederation         43 non-null     object
 9   url                   43 non-null     object
dtypes: int64(1), object(9)
memory usage: 27.8 KB

eventos = pd.read_csv('/Users/andrewferreira/Downloads/archive (1)/game_events.csv')
eventos.head()

	game_event_id	date	game_id	minute	type	club_id	player_id	description	player_in_id	player_assist_id
0	2f41da30c471492e7d4a984951671677	2012-08-05	2211607	77	Cards	610	4425	1. Yellow card , Mass confrontation	NaN	NaN
1	a72f7186d132775f234d3e2f7bc0ed5b	2012-08-05	2211607	77	Cards	383	33210	1. Yellow card , Mass confrontation	NaN	NaN
2	b2d721eaed4692a5c59a92323689ef18	2012-08-05	2211607	3	Goals	383	36500	, Header, 1. Tournament Goal Assist: , Corner,...	NaN	56416.0
3	aef768899cedac0c9a650980219075a2	2012-08-05	2211607	53	Goals	383	36500	, Right-footed shot, 2. Tournament Goal Assist...	NaN	146258.0
4	5d6d9533023057b6619ecd145a038bbe	2012-08-05	2211607	74	Substitutions	383	36500	, Not reported	49499.0	NaN

eventos.info(memory_usage='deep')

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 681448 entries, 0 to 681447
Data columns (total 10 columns):
 #   Column            Non-Null Count   Dtype  
---  ------            --------------   -----  
 0   game_event_id     681448 non-null  object 
 1   date              681448 non-null  object 
 2   game_id           681448 non-null  int64  
 3   minute            681448 non-null  int64  
 4   type              681448 non-null  object 
 5   club_id           681448 non-null  int64  
 6   player_id         681448 non-null  int64  
 7   description       344096 non-null  object 
 8   player_in_id      429349 non-null  float64
 9   player_assist_id  32931 non-null   float64
dtypes: float64(2), int64(4), object(4)
memory usage: 214.9 MB

eventos.info(memory_usage='deep')

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 681448 entries, 0 to 681447
Data columns (total 10 columns):
 #   Column            Non-Null Count   Dtype  
---  ------            --------------   -----  
 0   game_event_id     681448 non-null  object 
 1   date              681448 non-null  object 
 2   game_id           681448 non-null  int64  
 3   minute            681448 non-null  int64  
 4   type              681448 non-null  object 
 5   club_id           681448 non-null  int64  
 6   player_id         681448 non-null  int64  
 7   description       344096 non-null  object 
 8   player_in_id      429349 non-null  float64
 9   player_assist_id  32931 non-null   float64
dtypes: float64(2), int64(4), object(4)
memory usage: 214.9 MB

escalados = pd.read_csv('/Users/andrewferreira/Downloads/archive (1)/game_lineups.csv')
escalados.head()

	game_lineups_id	game_id	club_id	type	number	player_id	player_name	team_captain	position
0	b2dbe01c3656b06c8e23e9de714e26bb	2317258	610	substitutes	5	1443	Christian Poulsen	0	Defensive Midfield
1	b50a3ec6d52fd1490aab42042ac4f738	2317258	610	starting_lineup	4	5017	Niklas Moisander	0	Centre-Back
2	7d890e6d0ff8af84b065839966a0ec81	2317258	1090	substitutes	11	9602	Maarten Martens	0	Left Winger
3	8c355268678b9bbc7084221b1f0fde36	2317258	610	starting_lineup	17	12282	Daley Blind	0	Left-Back
4	76193074d549e5fdce4cdcbba0d66247	2317258	1090	starting_lineup	23	25427	Roy Beerens	0	Right Winger

escalados.info(memory_usage='deep')

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2303206 entries, 0 to 2303205
Data columns (total 9 columns):
 #   Column           Dtype 
---  ------           ----- 
 0   game_lineups_id  object
 1   game_id          int64 
 2   club_id          int64 
 3   type             object
 4   number           object
 5   player_id        int64 
 6   player_name      object
 7   team_captain     int64 
 8   position         object
dtypes: int64(4), object(5)
memory usage: 867.3 MB

jogos = pd.read_csv('/Users/andrewferreira/Downloads/archive (1)/games.csv')
jogos.head()

	game_id	competition_id	season	round	date	home_club_id	away_club_id	home_club_goals	away_club_goals	home_club_position	...	stadium	attendance	referee	url	home_club_formation	away_club_formation	home_club_name	away_club_name	aggregate	competition_type
0	2321044	L1	2013	2. Matchday	2013-08-18	16	23	2	1	1.0	...	SIGNAL IDUNA PARK	80200.0	Peter Sippel	https://www.transfermarkt.co.uk/borussia-dortm...	4-2-3-1	4-3-2-1	Borussia Dortmund	Eintracht Braunschweig	2:1	domestic_league
1	2321060	L1	2013	3. Matchday	2013-08-25	23	24	0	2	18.0	...	EINTRACHT-Stadion	23325.0	Wolfgang Stark	https://www.transfermarkt.co.uk/eintracht-brau...	4-3-2-1	4-2-3-1	Eintracht Braunschweig	Eintracht Frankfurt Fußball AG	0:2	domestic_league
2	2321086	L1	2013	6. Matchday	2013-09-21	4	16	1	1	15.0	...	Max-Morlock-Stadion	50000.0	Knut Kircher	https://www.transfermarkt.co.uk/1-fc-nuremberg...	4-2-3-1	4-2-3-1	1.FC Nuremberg	Borussia Dortmund	1:1	domestic_league
3	2321152	L1	2013	11. Matchday	2013-11-02	44	33	0	2	7.0	...	Olympiastadion Berlin	69277.0	Günter Perl	https://www.transfermarkt.co.uk/hertha-bsc_fc-...	4-2-3-1	4-2-3-1	Hertha BSC	FC Schalke 04	0:2	domestic_league
4	2321205	L1	2013	17. Matchday	2013-12-21	41	39	2	3	14.0	...	Volksparkstadion	50000.0	Bastian Dankert	https://www.transfermarkt.co.uk/hamburger-sv_1...	4-2-3-1	4-4-2 Diamond	Hamburger SV	1. Fußball- und Sportverein Mainz 05	2:3	domestic_league

5 rows × 23 columns

jogos.info(memory_usage='deep')

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 66165 entries, 0 to 66164
Data columns (total 23 columns):
 #   Column                  Non-Null Count  Dtype  
---  ------                  --------------  -----  
 0   game_id                 66165 non-null  int64  
 1   competition_id          66165 non-null  object 
 2   season                  66165 non-null  int64  
 3   round                   66165 non-null  object 
 4   date                    66165 non-null  object 
 5   home_club_id            66165 non-null  int64  
 6   away_club_id            66165 non-null  int64  
 7   home_club_goals         66165 non-null  int64  
 8   away_club_goals         66165 non-null  int64  
 9   home_club_position      46445 non-null  float64
 10  away_club_position      46445 non-null  float64
 11  home_club_manager_name  65421 non-null  object 
 12  away_club_manager_name  65421 non-null  object 
 13  stadium                 65952 non-null  object 
 14  attendance              56502 non-null  float64
 15  referee                 65570 non-null  object 
 16  url                     66165 non-null  object 
 17  home_club_formation     59889 non-null  object 
 18  away_club_formation     60037 non-null  object 
 19  home_club_name          54787 non-null  object 
 20  away_club_name          56022 non-null  object 
 21  aggregate               66165 non-null  object 
 22  competition_type        66165 non-null  object 
dtypes: float64(3), int64(6), object(14)
memory usage: 70.9 MB

valor = pd.read_csv('/Users/andrewferreira/Downloads/archive (1)/player_valuations.csv')
valor.head()

	player_id	date	market_value_in_eur	current_club_id	player_club_domestic_competition_id
0	405973	2000-01-20	150000	3057	BE1
1	342216	2001-07-20	100000	1241	SC1
2	3132	2003-12-09	400000	126	TR1
3	6893	2003-12-15	900000	984	GB1
4	10	2004-10-04	7000000	398	IT1

valor.info(memory_usage='deep')

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 464801 entries, 0 to 464800
Data columns (total 5 columns):
 #   Column                               Non-Null Count   Dtype 
---  ------                               --------------   ----- 
 0   player_id                            464801 non-null  int64 
 1   date                                 464801 non-null  object
 2   market_value_in_eur                  464801 non-null  int64 
 3   current_club_id                      464801 non-null  int64 
 4   player_club_domestic_competition_id  464801 non-null  object
dtypes: int64(3), object(2)
memory usage: 66.9 MB

jogadores = pd.read_csv('/Users/andrewferreira/Downloads/archive (1)/players.csv')
jogadores.head()

	player_id	first_name	last_name	name	last_season	current_club_id	player_code	country_of_birth	city_of_birth	country_of_citizenship	...	foot	height_in_cm	contract_expiration_date	agent_name	image_url	url	current_club_domestic_competition_id	current_club_name	market_value_in_eur	highest_market_value_in_eur
0	10	Miroslav	Klose	Miroslav Klose	2015	398	miroslav-klose	Poland	Opole	Germany	...	right	184.0	NaN	ASBW Sport Marketing	https://img.a.transfermarkt.technology/portrai...	https://www.transfermarkt.co.uk/miroslav-klose...	IT1	Società Sportiva Lazio S.p.A.	1000000.0	30000000.0
1	26	Roman	Weidenfeller	Roman Weidenfeller	2017	16	roman-weidenfeller	Germany	Diez	Germany	...	left	190.0	NaN	Neubauer 13 GmbH	https://img.a.transfermarkt.technology/portrai...	https://www.transfermarkt.co.uk/roman-weidenfe...	L1	Borussia Dortmund	750000.0	8000000.0
2	65	Dimitar	Berbatov	Dimitar Berbatov	2015	1091	dimitar-berbatov	Bulgaria	Blagoevgrad	Bulgaria	...	NaN	NaN	NaN	CSKA-AS-23 Ltd.	https://img.a.transfermarkt.technology/portrai...	https://www.transfermarkt.co.uk/dimitar-berbat...	GR1	Panthessalonikios Athlitikos Omilos Konstantin...	1000000.0	34500000.0
3	77	NaN	Lúcio	Lúcio	2012	506	lucio	Brazil	Brasília	Brazil	...	NaN	NaN	NaN	NaN	https://img.a.transfermarkt.technology/portrai...	https://www.transfermarkt.co.uk/lucio/profil/s...	IT1	Juventus Football Club	200000.0	24500000.0
4	80	Tom	Starke	Tom Starke	2017	27	tom-starke	East Germany (GDR)	Freital	Germany	...	right	194.0	NaN	IFM	https://img.a.transfermarkt.technology/portrai...	https://www.transfermarkt.co.uk/tom-starke/pro...	L1	FC Bayern München	100000.0	3000000.0

5 rows × 23 columns

jogadores.info(memory_usage='deep')

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 30383 entries, 0 to 30382
Data columns (total 23 columns):
 #   Column                                Non-Null Count  Dtype  
---  ------                                --------------  -----  
 0   player_id                             30383 non-null  int64  
 1   first_name                            28403 non-null  object 
 2   last_name                             30383 non-null  object 
 3   name                                  30383 non-null  object 
 4   last_season                           30383 non-null  int64  
 5   current_club_id                       30383 non-null  int64  
 6   player_code                           30383 non-null  object 
 7   country_of_birth                      27697 non-null  object 
 8   city_of_birth                         28196 non-null  object 
 9   country_of_citizenship                29829 non-null  object 
 10  date_of_birth                         30337 non-null  object 
 11  sub_position                          30209 non-null  object 
 12  position                              30383 non-null  object 
 13  foot                                  28013 non-null  object 
 14  height_in_cm                          28306 non-null  float64
 15  contract_expiration_date              18924 non-null  object 
 16  agent_name                            15060 non-null  object 
 17  image_url                             30383 non-null  object 
 18  url                                   30383 non-null  object 
 19  current_club_domestic_competition_id  30383 non-null  object 
 20  current_club_name                     30383 non-null  object 
 21  market_value_in_eur                   29260 non-null  float64
 22  highest_market_value_in_eur           29260 non-null  float64
dtypes: float64(3), int64(3), object(17)
memory usage: 37.4 MB

After loading all libraries and data, we created the following dataframes:

- presencas - What are the games and competitions in which the players participated, as well as some statistics.

- clube_jogos - It contains the game identifier as well as the club and their opponent.

- clubes - Club identifier and the identifier of which the domentica competition.

- competicoes - Competition identifier.

- eventos - Contains the game and player identifier.

- escalados - Refers to the way the player interacted in this game. Whether it was a substitute used or a starter.

- jogos - It contains the game identifier, which club plays at home/away and the competition. 

- valor - Market value of the player in euros.

- jogadores - List with players, what is their club, as well as other information.

Note that we can see there are a lot of missing values in some of our dataframes. I will check out some of the missing values and see if it's relevant to change those missing values, delete them or simply just keep them in our dataframe.

Through the image we can see how all dataframes can be interconnected.

(Note that I used other names in my dataframes)

Index

1.3 Missing Values

cels_ausentes = presencas[presencas['player_name'].isnull()]
cels_ausentes

	appearance_id	game_id	player_id	player_club_id	player_current_club_id	date	player_name	competition_id	yellow_cards	red_cards	goals	assists	minutes_played
797980	3084062_380365	3084062	380365	16486	-1	2018-09-05	NaN	CDR	1	0	0	0	90

presencas = presencas.dropna()

presencas.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 1524125 entries, 0 to 1524125
Data columns (total 13 columns):
 #   Column                  Non-Null Count    Dtype 
---  ------                  --------------    ----- 
 0   appearance_id           1524125 non-null  object
 1   game_id                 1524125 non-null  int64 
 2   player_id               1524125 non-null  int64 
 3   player_club_id          1524125 non-null  int64 
 4   player_current_club_id  1524125 non-null  int64 
 5   date                    1524125 non-null  object
 6   player_name             1524125 non-null  object
 7   competition_id          1524125 non-null  object
 8   yellow_cards            1524125 non-null  int64 
 9   red_cards               1524125 non-null  int64 
 10  goals                   1524125 non-null  int64 
 11  assists                 1524125 non-null  int64 
 12  minutes_played          1524125 non-null  int64 
dtypes: int64(9), object(4)
memory usage: 162.8+ MB

cels_ausentes1 = clube_jogos[clube_jogos['own_position'].isnull()]
cels_ausentes1.head()

	game_id	club_id	own_goals	own_position	own_manager_name	opponent_id	opponent_goals	opponent_position	opponent_manager_name	hosting	is_win
0	2320450	1468	0	NaN	Holger Bachthaler	24	2	NaN	Armin Veh	Home	0
1	2320460	1	3	NaN	Jürgen Luginger	86	1	NaN	Robin Dutt	Home	1
2	2320472	2036	4	NaN	Frank Schmidt	72	5	NaN	Alexander Schmidt	Home	0
13	2321976	10604	1	NaN	Branko Nisevic	448	4	NaN	Marko Nikolic	Home	0
14	2321990	1257	2	NaN	Dan Theis	2275	0	NaN	Marko Rajamäki	Home	1

cels_ausentes2 = clube_jogos[clube_jogos['own_manager_name'].isnull()]
cels_ausentes2.head()

	game_id	club_id	own_goals	own_position	own_manager_name	opponent_id	opponent_goals	opponent_position	opponent_manager_name	hosting	is_win
167	2366500	20417	5	NaN	NaN	22235	0	NaN	NaN	Home	1
169	2366512	24391	0	NaN	NaN	369	5	NaN	NaN	Home	0
194	2381474	3609	3	NaN	NaN	6672	0	NaN	NaN	Home	1
212	2422830	265	1	4.0	NaN	21957	0	16.0	NaN	Home	1
344	2481203	11259	10	NaN	NaN	18669	9	NaN	NaN	Home	1

cels_ausentes3 = clubes[clubes['average_age'].isnull()]
cels_ausentes3.head()

	club_id	club_code	name	domestic_competition_id	total_market_value	squad_size	average_age	foreigners_number	foreigners_percentage	national_team_players	stadium_name	stadium_seats	net_transfer_record	coach_name	last_season	filename	url
11	21957	ao-platanias	AO Platanias	GR1	NaN	0	NaN	0	NaN	0	Dimotiko Gipedo Perivolia	3700	+-0	NaN	2017	../data/raw/transfermarkt-scraper/2017/clubs.j...	https://www.transfermarkt.co.uk/ao-platanias/s...
25	6996	goverla-uzhgorod	Goverla Uzhgorod (- 2016)	UKR1	NaN	0	NaN	0	NaN	0	Avangard	10640	+-0	NaN	2015	../data/raw/transfermarkt-scraper/2015/clubs.j...	https://www.transfermarkt.co.uk/goverla-uzhgor...
38	2477	karpaty-lviv	Karpaty Lviv (-2021)	UKR1	NaN	0	NaN	0	NaN	0	Stadion Ukraina	28051	+-0	NaN	2019	../data/raw/transfermarkt-scraper/2019/clubs.j...	https://www.transfermarkt.co.uk/karpaty-lviv/s...
53	566	beerschot-ac	Beerschot AC	BE1	NaN	0	NaN	0	NaN	0	Olympisch Stadion	12771	+-0	NaN	2012	../data/raw/transfermarkt-scraper/2012/clubs.j...	https://www.transfermarkt.co.uk/beerschot-ac/s...
68	1411	raec-mons	RAEC Mons (- 2015)	BE1	NaN	0	NaN	0	NaN	0	Stade Charles Tondreau	12662	+-0	NaN	2013	../data/raw/transfermarkt-scraper/2013/clubs.j...	https://www.transfermarkt.co.uk/raec-mons/star...

cels_ausentes4 = competicoes[competicoes['country_name'].isnull()]
cels_ausentes4.head()

	competition_id	competition_code	name	sub_type	type	country_id	country_name	domestic_league_code	confederation	url
6	USC	uefa-super-cup	uefa-super-cup	uefa_super_cup	other	-1	NaN	NaN	europa	https://www.transfermarkt.co.uk/uefa-super-cup...
8	EL	europa-league	europa-league	europa_league	international_cup	-1	NaN	NaN	europa	https://www.transfermarkt.co.uk/europa-league/...
17	ELQ	europa-league-qualifikation	europa-league-qualifikation	europa_league_qualifying	international_cup	-1	NaN	NaN	europa	https://www.transfermarkt.co.uk/europa-league-...
20	ECLQ	uefa-europa-conference-league-qualifikation	uefa-europa-conference-league-qualifikation	uefa_europa_conference_league_qualifiers	international_cup	-1	NaN	NaN	europa	https://www.transfermarkt.co.uk/uefa-europa-co...
26	CL	uefa-champions-league	uefa-champions-league	uefa_champions_league	international_cup	-1	NaN	NaN	europa	https://www.transfermarkt.co.uk/uefa-champions...

As mentioned above, there are several missing data in many dataframes.

After printing some of the plots that contain missing data, I drew some conclusions:

• There are absent values in country_name that are part of international competitions, so no country exists.

• average_age contains absent data because they are part of teams that are not so recognised.

• position can contain absent values by precisely the teams inserted also don't make part of top scale teams.

I only removed one missing item on the dataframe I want to study 'presencas'. After reflection, I do not think it is necessary to make any changes to the missing data, at least at this time. The main dataframe that I intend to study contains all the data.

If necessary, I will replace or remove missing data.

Index

1.4 Data enrichment

I will change to date format some of the types of data that refer precisely to the date.

presencas['date'] = pd.to_datetime(presencas['date'], format="%Y/%m/%d")
valor['date'] = pd.to_datetime(valor['date'], format="%Y/%m/%d")
jogadores['date'] = pd.to_datetime(eventos['date'], format="%Y/%m/%d")
eventos['date'] = pd.to_datetime(eventos['date'], format="%Y/%m/%d")

As the dataframes we load require us to use a lot of memory from our system, we will change some of the types of data so that it optimizes the memory usage considerably to the system.

presencas['player_name'] = presencas['player_name'].astype('category') 
presencas['competition_id'] = presencas['competition_id'].astype('category') 
clube_jogos['own_manager_name'] = clube_jogos['own_manager_name'].astype('category') 
clube_jogos['opponent_manager_name'] = clube_jogos['opponent_manager_name'].astype('category') 
clube_jogos['hosting'] = clube_jogos['hosting'].astype('category') 
clubes['club_code'] = clubes['club_code'].astype('category')
clubes['name'] = clubes['name'].astype('category')
eventos['type'] = eventos['type'].astype('category')
eventos['description'] = eventos['description'].astype('category')
escalados['type'] = escalados['type'].astype('category')
escalados['player_name'] = escalados['player_name'].astype('category')
escalados['position'] = escalados['position'].astype('category')
jogos['competition_id'] = jogos['competition_id'].astype('category')
jogos['round'] = jogos['round'].astype('category')
jogos['referee'] = jogos['referee'].astype('category')
jogos['home_club_name'] = jogos['home_club_name'].astype('category')
jogos['away_club_name'] = jogos['away_club_name'].astype('category')
jogos['competition_type'] = jogos['competition_type'].astype('category')
valor['player_club_domestic_competition_id'] = valor['player_club_domestic_competition_id'].astype('category')
jogadores['first_name'] = jogadores['first_name'].astype('category')
jogadores['last_name'] = jogadores['last_name'].astype('category')
jogadores['name'] = jogadores['name'].astype('category')
jogadores['player_code'] = jogadores['player_code'].astype('category')
jogadores['country_of_birth'] = jogadores['country_of_birth'].astype('category')
jogadores['city_of_birth'] = jogadores['city_of_birth'].astype('category')
jogadores['country_of_citizenship'] = jogadores['country_of_citizenship'].astype('category')
jogadores['agent_name'] = jogadores['agent_name'].astype('category')
jogadores['current_club_domestic_competition_id'] = jogadores['current_club_domestic_competition_id'].astype('category')
jogadores['current_club_name'] = jogadores['current_club_name'].astype('category')

jogadores.info(memory_usage='deep')

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 30383 entries, 0 to 30382
Data columns (total 24 columns):
 #   Column                                Non-Null Count  Dtype         
---  ------                                --------------  -----         
 0   player_id                             30383 non-null  int64         
 1   first_name                            28403 non-null  category      
 2   last_name                             30383 non-null  category      
 3   name                                  30383 non-null  category      
 4   last_season                           30383 non-null  int64         
 5   current_club_id                       30383 non-null  int64         
 6   player_code                           30383 non-null  category      
 7   country_of_birth                      27697 non-null  category      
 8   city_of_birth                         28196 non-null  category      
 9   country_of_citizenship                29829 non-null  category      
 10  date_of_birth                         30337 non-null  object        
 11  sub_position                          30209 non-null  object        
 12  position                              30383 non-null  object        
 13  foot                                  28013 non-null  object        
 14  height_in_cm                          28306 non-null  float64       
 15  contract_expiration_date              18924 non-null  object        
 16  agent_name                            15060 non-null  category      
 17  image_url                             30383 non-null  object        
 18  url                                   30383 non-null  object        
 19  current_club_domestic_competition_id  30383 non-null  category      
 20  current_club_name                     30383 non-null  category      
 21  market_value_in_eur                   29260 non-null  float64       
 22  highest_market_value_in_eur           29260 non-null  float64       
 23  date                                  30383 non-null  datetime64[ns]
dtypes: category(10), datetime64[ns](1), float64(3), int64(3), object(7)
memory usage: 28.9 MB

Just an example:

• Before, the dataframe jogadores were using 37.4 MB in the system, after changing the data it is using 28.9 MB. It seems little, but in such a large dataframe this will make a lot of difference.

Index

2. Data Analysis

After some changes made to the various dataframes, the time has come to start analysing the data.

In the first instance I will print some relevant information and then groupby players with goals, assists, how many minutes they played and cards.

presencas.head()

	appearance_id	game_id	player_id	player_club_id	player_current_club_id	date	player_name	competition_id	yellow_cards	red_cards	goals	assists	minutes_played
0	2231978_38004	2231978	38004	853	235	2012-07-03	Aurélien Joachim	CLQ	0	0	2	0	90
1	2233748_79232	2233748	79232	8841	2698	2012-07-05	Ruslan Abyshov	ELQ	0	0	0	0	90
2	2234413_42792	2234413	42792	6251	465	2012-07-05	Sander Puri	ELQ	0	0	0	0	45
3	2234418_73333	2234418	73333	1274	6646	2012-07-05	Vegar Hedenstad	ELQ	0	0	0	0	90
4	2234421_122011	2234421	122011	195	3008	2012-07-05	Markus Henriksen	ELQ	0	0	0	1	90

presencas.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 1524125 entries, 0 to 1524125
Data columns (total 13 columns):
 #   Column                  Non-Null Count    Dtype         
---  ------                  --------------    -----         
 0   appearance_id           1524125 non-null  object        
 1   game_id                 1524125 non-null  int64         
 2   player_id               1524125 non-null  int64         
 3   player_club_id          1524125 non-null  int64         
 4   player_current_club_id  1524125 non-null  int64         
 5   date                    1524125 non-null  datetime64[ns]
 6   player_name             1524125 non-null  category      
 7   competition_id          1524125 non-null  category      
 8   yellow_cards            1524125 non-null  int64         
 9   red_cards               1524125 non-null  int64         
 10  goals                   1524125 non-null  int64         
 11  assists                 1524125 non-null  int64         
 12  minutes_played          1524125 non-null  int64         
dtypes: category(2), datetime64[ns](1), int64(9), object(1)
memory usage: 144.6+ MB

presencas.shape

(1524125, 13)

#checking that we don't have any missing values
presencas.isnull().sum()

appearance_id             0
game_id                   0
player_id                 0
player_club_id            0
player_current_club_id    0
date                      0
player_name               0
competition_id            0
yellow_cards              0
red_cards                 0
goals                     0
assists                   0
minutes_played            0
dtype: int64

presencas.describe()

	game_id	player_id	player_club_id	player_current_club_id	yellow_cards	red_cards	goals	assists	minutes_played
count	1.524125e+06	1.524125e+06	1.524125e+06	1.524125e+06	1.524125e+06	1.524125e+06	1.524125e+06	1.524125e+06	1.524125e+06
mean	3.033598e+06	1.833277e+05	2.977173e+03	3.619215e+03	1.487247e-01	3.815960e-03	9.630116e-02	7.590322e-02	6.959456e+01
std	5.694835e+05	1.682505e+05	7.550138e+03	9.226995e+03	3.671985e-01	6.165550e-02	3.317028e-01	2.864926e-01	2.978741e+01
min	2.211607e+06	1.000000e+01	1.000000e+00	3.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	1.000000e+00
25%	2.532476e+06	5.492800e+04	2.810000e+02	3.360000e+02	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	5.100000e+01
50%	2.942764e+06	1.291290e+05	8.330000e+02	9.310000e+02	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	9.000000e+01
75%	3.461749e+06	2.651530e+05	2.441000e+03	2.687000e+03	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	9.000000e+01
max	4.271739e+06	1.186012e+06	1.016340e+05	8.367800e+04	2.000000e+00	1.000000e+00	6.000000e+00	6.000000e+00	1.350000e+02

presencas.nunique()

appearance_id             1524125
game_id                     60114
player_id                   23858
player_club_id               1022
player_current_club_id        424
date                         3380
player_name                 23363
competition_id                 43
yellow_cards                    3
red_cards                       2
goals                           7
assists                         7
minutes_played                121
dtype: int64

presencas["date"].max()

Timestamp('2024-01-22 00:00:00')

presencas["date"].min()

Timestamp('2012-07-03 00:00:00')

So we can see that our dataframe contains info from almost 12 years

Groupby player_name with goals, assists, yellow_cards, red_cards and minutes_played

presencas_novo = presencas.groupby(['player_name'])[['goals', 'assists', 'yellow_cards', 'red_cards', 'minutes_played']].agg(['sum'])
presencas_novo.reset_index(inplace=True)
presencas_novo.head()

	player_name	goals	assists	yellow_cards	red_cards	minutes_played
		sum	sum	sum	sum	sum
0	A.J. Soares	1	0	2	0	421
1	AJ Leitch-Smith	7	6	1	0	1700
2	Aaron Appindangoyé	1	2	12	0	8084
3	Aaron Bastiaans	2	0	0	0	144
4	Aaron Boupendza	22	1	7	0	2586

#change column names in our new dataframe
presencas_novo.columns = ['player_name', 'total_goals', 'total_assists', 'total_yellow_cards', 'total_red_cards', 'total_minutes_played']
presencas_novo.head()

	player_name	total_goals	total_assists	total_yellow_cards	total_red_cards	total_minutes_played
0	A.J. Soares	1	0	2	0	421
1	AJ Leitch-Smith	7	6	1	0	1700
2	Aaron Appindangoyé	1	2	12	0	8084
3	Aaron Bastiaans	2	0	0	0	144
4	Aaron Boupendza	22	1	7	0	2586

presencas_novo.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 23363 entries, 0 to 23362
Data columns (total 6 columns):
 #   Column                Non-Null Count  Dtype   
---  ------                --------------  -----   
 0   player_name           23363 non-null  category
 1   total_goals           23363 non-null  int64   
 2   total_assists         23363 non-null  int64   
 3   total_yellow_cards    23363 non-null  int64   
 4   total_red_cards       23363 non-null  int64   
 5   total_minutes_played  23363 non-null  int64   
dtypes: category(1), int64(5)
memory usage: 1.6 MB

Index

2.1 Top Goalscorers

Let's check out who were the 20 top goalscorers

golos_20 = presencas_novo.sort_values(by="total_goals", ascending=False).head(20)
golos_20

	player_name	total_goals	total_assists	total_yellow_cards	total_red_cards	total_minutes_played
18599	Robert Lewandowski	453	111	53	1	46014
12804	Lionel Messi	443	219	49	1	43543
4264	Cristiano Ronaldo	432	114	64	4	41150
13187	Luis Suárez	308	157	114	1	42668
8635	Harry Kane	294	69	45	0	34993
17896	Pierre-Emerick Aubameyang	277	76	33	1	36593
15612	Mohamed Salah	254	117	19	0	36834
18891	Romelu Lukaku	250	75	43	1	36808
4030	Ciro Immobile	250	66	71	2	33400
11614	Karim Benzema	249	121	7	0	34639
5998	Edinson Cavani	232	47	48	0	27906
22514	Wissam Ben Yedder	228	70	26	0	31960
12318	Kylian Mbappé	228	97	40	1	23155
19867	Sergio Agüero	220	57	27	1	22962
1907	Antoine Griezmann	213	99	58	2	38927
16180	Neymar	212	143	93	2	28364
995	Alexandre Lacazette	209	70	51	2	33513
5994	Edin Dzeko	207	100	60	2	34558
14782	Mauro Icardi	203	55	21	0	27983
23116	Zlatan Ibrahimović	196	78	42	4	21715

# Plotting the bar chart
sorted_data = golos_20.sort_values(by='total_goals', ascending=True)
plt.barh(sorted_data['player_name'], sorted_data['total_goals'], color='blue')
plt.xlabel('Number of Goals')
plt.ylabel('Players')
plt.title('Top 20 Goal scorers')
plt.xticks(rotation=45)
plt.show()

Index

2.2 Top Assisters

Let's check who's part of the 20 biggest assisters

assistencias_20 = presencas_novo.sort_values(by="total_assists", ascending=False).head(20)
assistencias_20

	player_name	total_goals	total_assists	total_yellow_cards	total_red_cards	total_minutes_played
12804	Lionel Messi	443	219	49	1	43543
11840	Kevin De Bruyne	117	195	48	0	35054
21105	Thomas Müller	166	190	26	1	34384
5883	Dusan Tadic	171	188	47	0	40523
23237	Ángel Di María	117	163	35	4	31286
13187	Luis Suárez	308	157	114	1	42668
16180	Neymar	212	143	93	2	28364
8549	Hakim Ziyech	114	135	54	0	29480
3847	Christian Eriksen	92	131	28	0	35309
5434	Dimitri Payet	96	128	55	3	29707
13935	Marco Reus	167	124	40	1	30913
18121	Raheem Sterling	160	122	60	0	36463
11614	Karim Benzema	249	121	7	0	34639
4966	David Silva	70	120	48	1	31617
5842	Dries Mertens	177	119	58	1	30477
8742	Henrikh Mkhitaryan	128	119	58	0	35698
20613	Steven Berghuis	133	117	60	2	29578
15612	Mohamed Salah	254	117	19	0	36834
3242	Bruno Fernandes	136	116	100	0	35238
17554	Paulinho	166	115	180	4	71566

# Plotting the bar chart
sorted_data_assists = assistencias_20.sort_values(by='total_assists', ascending=True)
plt.barh(sorted_data_assists['player_name'], sorted_data_assists['total_assists'], color='blue')
plt.xlabel('Number of Assists')
plt.ylabel('Players')
plt.title('Top 20 Goal Assisters')
plt.xticks(rotation=45)
plt.show()

Index

2.3 Contribution for Goal

presencas_novo["total_goals_assists"] = presencas_novo["total_goals"] + presencas_novo["total_assists"]
presencas_novo_goals_assists = presencas_novo.sort_values(by="total_goals_assists", ascending=False).head(20)
presencas_novo_goals_assists['total_goals_assists'] = presencas_novo_goals_assists['total_goals_assists'].astype(str)

presencas_novo_goals_assists = presencas_novo_goals_assists.sort_values(by="total_goals_assists", ascending=True)
most_20_total_goals_assists = px.bar(presencas_novo_goals_assists, x=["total_goals", "total_assists"], y='player_name', orientation='h', title="Top 20 Players by Total Goal Contribution (Goals + Assists)" , text="total_goals_assists")
most_20_total_goals_assists.show()
most_20_total_goals_assists.write_image("plot.png", engine="kaleido")

Index

2.4 Time needed to score a goal

presencas_novo_goals_assists.head()

	player_name	total_goals	total_assists	total_yellow_cards	total_red_cards	total_minutes_played	total_goals_assists
13935	Marco Reus	167	124	40	1	30913	291
5842	Dries Mertens	177	119	58	1	30477	296
22514	Wissam Ben Yedder	228	70	26	0	31960	298
5994	Edin Dzeko	207	100	60	2	34558	307
11840	Kevin De Bruyne	117	195	48	0	35054	312

presencas_novo_goals_assists['goal_per_minute'] = presencas_novo_goals_assists['total_minutes_played'] / presencas_novo_goals_assists['total_goals']
presencas_novo_goals_assists['goal_per_minute'] = presencas_novo_goals_assists['goal_per_minute'].round(2)
goals_minute = presencas_novo_goals_assists.sort_values(by="goal_per_minute", ascending=True).head(20)
goals_minute

	player_name	total_goals	total_assists	total_yellow_cards	total_red_cards	total_minutes_played	total_goals_assists	goal_per_minute
4264	Cristiano Ronaldo	432	114	64	4	41150	546	95.25
12804	Lionel Messi	443	219	49	1	43543	662	98.29
12318	Kylian Mbappé	228	97	40	1	23155	325	101.56
18599	Robert Lewandowski	453	111	53	1	46014	564	101.58
8635	Harry Kane	294	69	45	0	34993	363	119.02
17896	Pierre-Emerick Aubameyang	277	76	33	1	36593	353	132.10
4030	Ciro Immobile	250	66	71	2	33400	316	133.60
16180	Neymar	212	143	93	2	28364	355	133.79
13187	Luis Suárez	308	157	114	1	42668	465	138.53
11614	Karim Benzema	249	121	7	0	34639	370	139.11
22514	Wissam Ben Yedder	228	70	26	0	31960	298	140.18
15612	Mohamed Salah	254	117	19	0	36834	371	145.02
18891	Romelu Lukaku	250	75	43	1	36808	325	147.23
5994	Edin Dzeko	207	100	60	2	34558	307	166.95
5842	Dries Mertens	177	119	58	1	30477	296	172.19
1907	Antoine Griezmann	213	99	58	2	38927	312	182.76
13935	Marco Reus	167	124	40	1	30913	291	185.11
21105	Thomas Müller	166	190	26	1	34384	356	207.13
5883	Dusan Tadic	171	188	47	0	40523	359	236.98
11840	Kevin De Bruyne	117	195	48	0	35054	312	299.61

# Plotting the bar chart
sorted_data_ga = goals_minute.sort_values(by='goal_per_minute', ascending = False)
plt.barh(sorted_data_ga['player_name'], sorted_data_ga['goal_per_minute'], color='blue')
plt.xlabel('Goal per minute')
plt.ylabel('Players')
plt.title('Top 20 Players by goal per minute')
plt.xticks(rotation=45)
plt.show()

Index

2.5 Time needed to assist

presencas_novo_goals_assists['assist_per_minute'] = presencas_novo_goals_assists['total_minutes_played'] / presencas_novo_goals_assists['total_assists']
presencas_novo_goals_assists['assist_per_minute'] = presencas_novo_goals_assists['assist_per_minute'].round(2)
assist_minute = presencas_novo_goals_assists.sort_values(by="assist_per_minute", ascending=True).head(20)
assist_minute

	player_name	total_goals	total_assists	total_yellow_cards	total_red_cards	total_minutes_played	total_goals_assists	goal_per_minute	assist_per_minute
11840	Kevin De Bruyne	117	195	48	0	35054	312	299.61	179.76
21105	Thomas Müller	166	190	26	1	34384	356	207.13	180.97
16180	Neymar	212	143	93	2	28364	355	133.79	198.35
12804	Lionel Messi	443	219	49	1	43543	662	98.29	198.83
5883	Dusan Tadic	171	188	47	0	40523	359	236.98	215.55
12318	Kylian Mbappé	228	97	40	1	23155	325	101.56	238.71
13935	Marco Reus	167	124	40	1	30913	291	185.11	249.30
5842	Dries Mertens	177	119	58	1	30477	296	172.19	256.11
13187	Luis Suárez	308	157	114	1	42668	465	138.53	271.77
11614	Karim Benzema	249	121	7	0	34639	370	139.11	286.27
15612	Mohamed Salah	254	117	19	0	36834	371	145.02	314.82
5994	Edin Dzeko	207	100	60	2	34558	307	166.95	345.58
4264	Cristiano Ronaldo	432	114	64	4	41150	546	95.25	360.96
1907	Antoine Griezmann	213	99	58	2	38927	312	182.76	393.20
18599	Robert Lewandowski	453	111	53	1	46014	564	101.58	414.54
22514	Wissam Ben Yedder	228	70	26	0	31960	298	140.18	456.57
17896	Pierre-Emerick Aubameyang	277	76	33	1	36593	353	132.10	481.49
18891	Romelu Lukaku	250	75	43	1	36808	325	147.23	490.77
4030	Ciro Immobile	250	66	71	2	33400	316	133.60	506.06
8635	Harry Kane	294	69	45	0	34993	363	119.02	507.14

# Plotting the bar chart
sorted_data_a = assist_minute.sort_values(by='assist_per_minute', ascending = False)
plt.barh(sorted_data_a['player_name'], sorted_data_a['assist_per_minute'], color='blue')
plt.xlabel('Assist per minute')
plt.ylabel('Players')
plt.title('Top 20 Players by assist per minute')
plt.xticks(rotation=45)
plt.show()

Index

2.6 Top 20 Players by Total Goal Contribution per minute (Goals + Assists)

presencas_novo_goals_assists["total_goals_assists_per_minute"] = presencas_novo_goals_assists["goal_per_minute"] + presencas_novo_goals_assists["assist_per_minute"]
presencas_novo_goals_assists = presencas_novo_goals_assists.sort_values(by="total_goals_assists_per_minute", ascending=False).head(20)
presencas_novo_goals_assists['total_goals_assists_per_minute'] = presencas_novo_goals_assists['total_goals_assists_per_minute'].astype(str)

presencas_novo_goals_assists = presencas_novo_goals_assists.sort_values(by="total_goals_assists_per_minute", ascending=False)
most_20_total_goals_assists_per_minute = px.bar(presencas_novo_goals_assists, x=["goal_per_minute", "assist_per_minute"], y='player_name', orientation='h', title="Top 20 Players by Total Goal Contribution per minute (Goals + Assists)" , text="total_goals_assists_per_minute")
most_20_total_goals_assists_per_minute.show()

#check which players are in the list of best goalscorers and assisters
merged_df = pd.merge(golos_20, assistencias_20, on='player_name', how='inner')
players_in_both = merged_df['player_name'].tolist()
players_in_both

['Lionel Messi', 'Luis Suárez', 'Mohamed Salah', 'Karim Benzema', 'Neymar']

After some studies and reading graphs we conclude that:

• The 3 best scorers are Robert Lewandowski, Lionel Messi and Cristiano Ronaldo.

• Of the twenty best goalscorers, all play forward.

• Only Messi, Suarez, Salah, Benzema and Neymar are both on the list of best scorer and assistant.

• Messi is the player who contributes the most to the goal (662 actions for a goal)

• Cristiano Ronaldo is the player who needs the least time to score a goal (95.25 minutes to score a goal. Almost 1 goal per game)

• Kevin De Bruyne needs only 179.76 minutes to make an assistance.

• Messi is the player who needs the least time to contribute to the goal.

• Despite being the player who needs the fewest minutes to score, Ronaldo needs an average of 456.21 minutes to contribute to the goal.

Index

2.7 Players with most yellow cards

amarelos_20 = presencas_novo.sort_values(by="total_yellow_cards", ascending=False).head(20)
amarelos_20

	player_name	total_goals	total_assists	total_yellow_cards	total_red_cards	total_minutes_played	total_goals_assists
4655	Danilo	106	70	201	4	80523	176
17554	Paulinho	166	115	180	4	71566	281
13851	Marcelo	52	72	172	8	76472	124
7058	Fernando	58	43	168	3	50717	101
19906	Sergio Ramos	70	19	140	6	36455	89
19877	Sergio Busquets	11	33	136	1	41597	44
6989	Felipe	20	9	133	7	38048	29
13957	Marco Verratti	10	51	128	1	26861	61
4411	Damián Suárez	6	22	123	3	26048	28
8299	Granit Xhaka	30	38	122	5	36790	68
23210	Álvaro González	10	14	121	3	31011	24
4505	Daniel Carvajal	11	66	121	1	34236	77
18261	Raúl García	112	48	118	1	28529	160
4464	Dani Parejo	74	89	118	2	42266	163
14274	Marten de Roon	29	24	117	1	38109	53
19034	Rubén Pérez	2	7	117	2	25450	9
11560	Kalidou Koulibaly	19	13	116	4	36578	32
22307	Víctor Sánchez	11	21	115	1	19852	32
13187	Luis Suárez	308	157	114	1	42668	465
8379	Guilherme	41	51	111	2	60474	92

# Plotting the bar chart
sorted_data_yellow = amarelos_20.sort_values(by='total_yellow_cards', ascending=True)
plt.barh(sorted_data_yellow['player_name'], sorted_data_yellow['total_yellow_cards'], color='yellow')
plt.xlabel('Number of Yellow Cards')
plt.ylabel('Players')
plt.title('Top 20 Players with most Yellow Cards')
plt.xticks(rotation=45)
plt.show()

Index

2.8 Players with most red cards

vermelhos_20 = presencas_novo.sort_values(by="total_red_cards", ascending=False).head(20)
vermelhos_20

	player_name	total_goals	total_assists	total_yellow_cards	total_red_cards	total_minutes_played	total_goals_assists
13851	Marcelo	52	72	172	8	76472	124
6989	Felipe	20	9	133	7	38048	29
6876	Fallou Diagne	7	2	35	7	11718	9
22864	Younès Belhanda	62	58	77	6	24483	120
8808	Hilton	9	6	32	6	25446	15
14044	Mario Balotelli	113	20	90	6	17839	133
4508	Daniel Congré	10	7	32	6	24625	17
21607	Téji Savanier	40	36	34	6	13132	76
6949	Federico Fazio	24	6	55	6	26739	30
18110	Rafik Halliche	2	2	22	6	6665	4
19906	Sergio Ramos	70	19	140	6	36455	89
5753	Domenico Berardi	131	88	88	6	26263	219
10498	Joey van den Berg	11	13	35	5	9811	24
952	Alexander Djiku	6	3	62	5	20419	9
20476	Stefan Mitrovic	13	4	64	5	24967	17
21094	Thomas Lam	19	8	44	5	15533	27
21891	Vedad Ibisevic	94	31	54	5	18445	125
2925	Birger Verstraete	12	8	55	5	15031	20
17404	Papy Djilobodji	21	7	69	5	24267	28
11197	Joãozinho	50	86	71	5	39669	136

# Plotting the bar chart
sorted_data_red = vermelhos_20.sort_values(by='total_red_cards', ascending=True)
plt.barh(sorted_data_red['player_name'], sorted_data_red['total_red_cards'], color='red')
plt.xlabel('Number of Red Cards')
plt.ylabel('Players')
plt.title('Top 20 Players with most Red Cards')
plt.xticks(rotation=45)
plt.show()

Index

2.9 Players with most cards (Yellow / Red)

presencas_novo['total_cards'] = presencas_novo['total_yellow_cards'] + presencas_novo['total_red_cards']
total_20 = presencas_novo.sort_values(by="total_cards", ascending=False).head(20)
total_20

	player_name	total_goals	total_assists	total_yellow_cards	total_red_cards	total_minutes_played	total_goals_assists	total_cards
4655	Danilo	106	70	201	4	80523	176	205
17554	Paulinho	166	115	180	4	71566	281	184
13851	Marcelo	52	72	172	8	76472	124	180
7058	Fernando	58	43	168	3	50717	101	171
19906	Sergio Ramos	70	19	140	6	36455	89	146
6989	Felipe	20	9	133	7	38048	29	140
19877	Sergio Busquets	11	33	136	1	41597	44	137
13957	Marco Verratti	10	51	128	1	26861	61	129
8299	Granit Xhaka	30	38	122	5	36790	68	127
4411	Damián Suárez	6	22	123	3	26048	28	126
23210	Álvaro González	10	14	121	3	31011	24	124
4505	Daniel Carvajal	11	66	121	1	34236	77	122
11560	Kalidou Koulibaly	19	13	116	4	36578	32	120
4464	Dani Parejo	74	89	118	2	42266	163	120
18261	Raúl García	112	48	118	1	28529	160	119
19034	Rubén Pérez	2	7	117	2	25450	9	119
14274	Marten de Roon	29	24	117	1	38109	53	118
22307	Víctor Sánchez	11	21	115	1	19852	32	116
11177	João Pedro	150	48	111	4	46671	198	115
13187	Luis Suárez	308	157	114	1	42668	465	115

# Plotting the bar chart
sorted_data_total_cards = total_20.sort_values(by='total_cards', ascending=True)
plt.barh(sorted_data_total_cards['player_name'], sorted_data_total_cards['total_cards'], color='green')
plt.xlabel('Number of Cards')
plt.ylabel('Players')
plt.title('Top 20 Players with most Cards')
plt.xticks(rotation=45)
plt.show()

#check which players are in the list of best goalscorers and assisters
merged_cards = pd.merge(amarelos_20, vermelhos_20, on='player_name', how='inner')
players_in_both1 = merged_cards['player_name'].tolist()
players_in_both1

['Marcelo', 'Sergio Ramos', 'Felipe']

After analysing the graphs we can see that:

• Most of the players who appear in the graphs play in a more retreated part of the field (Defenders, Defensive Midfield)

• Luis Suarez who is a forward, surprisingly figures on the list of players with more yellows

• Only 3 players appear in the top 20 of players with more yellow and red cards given.

Index

3. Contribution per year of the 3 best players (Goals / Assists)

In this topic I will compare how was the contribution to goal over the years of Ronaldo, Messi and Lewandowski.

presencas.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 1524125 entries, 0 to 1524125
Data columns (total 13 columns):
 #   Column                  Non-Null Count    Dtype         
---  ------                  --------------    -----         
 0   appearance_id           1524125 non-null  object        
 1   game_id                 1524125 non-null  int64         
 2   player_id               1524125 non-null  int64         
 3   player_club_id          1524125 non-null  int64         
 4   player_current_club_id  1524125 non-null  int64         
 5   date                    1524125 non-null  datetime64[ns]
 6   player_name             1524125 non-null  category      
 7   competition_id          1524125 non-null  category      
 8   yellow_cards            1524125 non-null  int64         
 9   red_cards               1524125 non-null  int64         
 10  goals                   1524125 non-null  int64         
 11  assists                 1524125 non-null  int64         
 12  minutes_played          1524125 non-null  int64         
dtypes: category(2), datetime64[ns](1), int64(9), object(1)
memory usage: 144.6+ MB

presencas["year"] = presencas["date"].dt.year
presencas["month"] = presencas["date"].dt.month

presencas.head()

	appearance_id	game_id	player_id	player_club_id	player_current_club_id	date	player_name	competition_id	yellow_cards	red_cards	goals	assists	minutes_played	year	month
0	2231978_38004	2231978	38004	853	235	2012-07-03	Aurélien Joachim	CLQ	0	0	2	0	90	2012	7
1	2233748_79232	2233748	79232	8841	2698	2012-07-05	Ruslan Abyshov	ELQ	0	0	0	0	90	2012	7
2	2234413_42792	2234413	42792	6251	465	2012-07-05	Sander Puri	ELQ	0	0	0	0	45	2012	7
3	2234418_73333	2234418	73333	1274	6646	2012-07-05	Vegar Hedenstad	ELQ	0	0	0	0	90	2012	7
4	2234421_122011	2234421	122011	195	3008	2012-07-05	Markus Henriksen	ELQ	0	0	0	1	90	2012	7

presencas.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 1524125 entries, 0 to 1524125
Data columns (total 15 columns):
 #   Column                  Non-Null Count    Dtype         
---  ------                  --------------    -----         
 0   appearance_id           1524125 non-null  object        
 1   game_id                 1524125 non-null  int64         
 2   player_id               1524125 non-null  int64         
 3   player_club_id          1524125 non-null  int64         
 4   player_current_club_id  1524125 non-null  int64         
 5   date                    1524125 non-null  datetime64[ns]
 6   player_name             1524125 non-null  category      
 7   competition_id          1524125 non-null  category      
 8   yellow_cards            1524125 non-null  int64         
 9   red_cards               1524125 non-null  int64         
 10  goals                   1524125 non-null  int64         
 11  assists                 1524125 non-null  int64         
 12  minutes_played          1524125 non-null  int64         
 13  year                    1524125 non-null  int64         
 14  month                   1524125 non-null  int64         
dtypes: category(2), datetime64[ns](1), int64(11), object(1)
memory usage: 167.8+ MB

presencas.head()

	appearance_id	game_id	player_id	player_club_id	player_current_club_id	date	player_name	competition_id	yellow_cards	red_cards	goals	assists	minutes_played	year	month
0	2231978_38004	2231978	38004	853	235	2012-07-03	Aurélien Joachim	CLQ	0	0	2	0	90	2012	7
1	2233748_79232	2233748	79232	8841	2698	2012-07-05	Ruslan Abyshov	ELQ	0	0	0	0	90	2012	7
2	2234413_42792	2234413	42792	6251	465	2012-07-05	Sander Puri	ELQ	0	0	0	0	45	2012	7
3	2234418_73333	2234418	73333	1274	6646	2012-07-05	Vegar Hedenstad	ELQ	0	0	0	0	90	2012	7
4	2234421_122011	2234421	122011	195	3008	2012-07-05	Markus Henriksen	ELQ	0	0	0	1	90	2012	7

lewa_present = "Robert Lewandowski" in presencas["player_name"].unique()
lewa_present

True

messi_present = "Lionel Messi" in presencas["player_name"].unique()
messi_present

True

ronaldo_present = "Cristiano Ronaldo" in presencas["player_name"].unique()
ronaldo_present

True

result = presencas.groupby(["player_name", "year"])[["goals", "assists"]].agg("sum").reset_index()
# Add a column for the total contribution (goals + assists)
result["contribution"] = result["goals"] + result["assists"]
result

	player_name	year	goals	assists	contribution
0	A.J. Soares	2012	0	0	0
1	A.J. Soares	2013	0	0	0
2	A.J. Soares	2014	0	0	0
3	A.J. Soares	2015	0	0	0
4	A.J. Soares	2016	1	0	1
...	...	...	...	...	...
303714	Αbdul Razak	2020	0	0	0
303715	Αbdul Razak	2021	0	0	0
303716	Αbdul Razak	2022	0	0	0
303717	Αbdul Razak	2023	0	0	0
303718	Αbdul Razak	2024	0	0	0

303719 rows × 5 columns

nomes_desejados = ["Lionel Messi", "Robert Lewandowski", "Cristiano Ronaldo"]

# Filtering the result only for the 3 desired names
result_filtered = result[result["player_name"].isin(nomes_desejados)]
result_filtered

	player_name	year	goals	assists	contribution
55432	Cristiano Ronaldo	2012	23	4	27
55433	Cristiano Ronaldo	2013	59	17	76
55434	Cristiano Ronaldo	2014	56	21	77
55435	Cristiano Ronaldo	2015	54	18	72
55436	Cristiano Ronaldo	2016	42	14	56
55437	Cristiano Ronaldo	2017	42	9	51
55438	Cristiano Ronaldo	2018	43	12	55
55439	Cristiano Ronaldo	2019	25	5	30
55440	Cristiano Ronaldo	2020	41	6	47
55441	Cristiano Ronaldo	2021	34	6	40
55442	Cristiano Ronaldo	2022	13	2	15
55443	Cristiano Ronaldo	2023	0	0	0
55444	Cristiano Ronaldo	2024	0	0	0
166452	Lionel Messi	2012	35	9	44
166453	Lionel Messi	2013	39	12	51
166454	Lionel Messi	2014	50	20	70
166455	Lionel Messi	2015	48	27	75
166456	Lionel Messi	2016	51	27	78
166457	Lionel Messi	2017	50	19	69
166458	Lionel Messi	2018	42	24	66
166459	Lionel Messi	2019	42	14	56
166460	Lionel Messi	2020	26	23	49
166461	Lionel Messi	2021	34	14	48
166462	Lionel Messi	2022	17	24	41
166463	Lionel Messi	2023	9	6	15
166464	Lionel Messi	2024	0	0	0
241787	Robert Lewandowski	2012	16	9	25
241788	Robert Lewandowski	2013	36	13	49
241789	Robert Lewandowski	2014	22	11	33
241790	Robert Lewandowski	2015	38	10	48
241791	Robert Lewandowski	2016	39	6	45
241792	Robert Lewandowski	2017	44	8	52
241793	Robert Lewandowski	2018	42	8	50
241794	Robert Lewandowski	2019	48	9	57
241795	Robert Lewandowski	2020	45	13	58
241796	Robert Lewandowski	2021	58	7	65
241797	Robert Lewandowski	2022	38	8	46
241798	Robert Lewandowski	2023	24	9	33
241799	Robert Lewandowski	2024	3	0	3

data = {
    "Cristiano Ronaldo": {
        "years": [2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024],
        "contributions": [27, 76, 77, 72, 56, 51, 55, 30, 47, 40, 15, 0, 0],
    },
    "Lionel Messi": {
        "years": [2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024],
        "contributions": [44, 51, 70, 75, 78, 69, 66, 56, 49, 48, 41, 15, 0],
    },
    "Robert Lewandowski": {
        "years": [2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024],
        "contributions": [25, 49, 33, 48, 45, 52, 50, 57, 58, 65, 46, 33, 3],
    }
}

# Plotting the data
bar_width = 0.25
index = np.arange(len(data["Cristiano Ronaldo"]["years"]))

fig, ax = plt.subplots(figsize=(12, 7))

# Bar for Cristiano Ronaldo
bar1 = ax.bar(index - bar_width, data["Cristiano Ronaldo"]["contributions"], bar_width, label='Cristiano Ronaldo')

# Bar for Lionel Messi
bar2 = ax.bar(index, data["Lionel Messi"]["contributions"], bar_width, label='Lionel Messi')

# Bar for Robert Lewandowski
bar3 = ax.bar(index + bar_width, data["Robert Lewandowski"]["contributions"], bar_width, label='Robert Lewandowski')

# Adding labels and title
ax.set_xlabel('Year')
ax.set_ylabel('Contributions (Goals + Assists)')
ax.set_title('Player Contributions Over the Years')
ax.set_xticks(index)
ax.set_xticklabels(data["Cristiano Ronaldo"]["years"])
ax.legend()

# Display the plot
plt.show()

Starting with the year 2023 and 2024, I believe that Ronaldo doesn't have statistics because we don't have Saudi Arabian's statistics, same happens to Messi and MLS.

This graph shows the goal contribution these 3 players had over the years. Note that Lewandowski had more contributions for goal when Ronaldo and Messi started to slow down. If it was for total goals I believe Cristiano would be top more times than just 2. Messi tops with 78 contributions in the year 2016 followed by Ronaldo in 2014 with 77 and 76 in 2013.

Index

4. Sporting Clube de Portugal

I'm going to talk a little about Sporting Clube de Portugal. In this project I'm not going to stretch much but in a next project I want to talk more about Sporting and the Portuguese League. In this analysis I will only talk about the market value of Sporting players, and what's the assistance of the team's games in recent years.

4.1 Average stadium assistance 2012-2023

jogos.head()

	game_id	competition_id	season	round	date	home_club_id	away_club_id	home_club_goals	away_club_goals	home_club_position	...	stadium	attendance	referee	url	home_club_formation	away_club_formation	home_club_name	away_club_name	aggregate	competition_type
0	2321044	L1	2013	2. Matchday	2013-08-18	16	23	2	1	1.0	...	SIGNAL IDUNA PARK	80200.0	Peter Sippel	https://www.transfermarkt.co.uk/borussia-dortm...	4-2-3-1	4-3-2-1	Borussia Dortmund	Eintracht Braunschweig	2:1	domestic_league
1	2321060	L1	2013	3. Matchday	2013-08-25	23	24	0	2	18.0	...	EINTRACHT-Stadion	23325.0	Wolfgang Stark	https://www.transfermarkt.co.uk/eintracht-brau...	4-3-2-1	4-2-3-1	Eintracht Braunschweig	Eintracht Frankfurt Fußball AG	0:2	domestic_league
2	2321086	L1	2013	6. Matchday	2013-09-21	4	16	1	1	15.0	...	Max-Morlock-Stadion	50000.0	Knut Kircher	https://www.transfermarkt.co.uk/1-fc-nuremberg...	4-2-3-1	4-2-3-1	1.FC Nuremberg	Borussia Dortmund	1:1	domestic_league
3	2321152	L1	2013	11. Matchday	2013-11-02	44	33	0	2	7.0	...	Olympiastadion Berlin	69277.0	Günter Perl	https://www.transfermarkt.co.uk/hertha-bsc_fc-...	4-2-3-1	4-2-3-1	Hertha BSC	FC Schalke 04	0:2	domestic_league
4	2321205	L1	2013	17. Matchday	2013-12-21	41	39	2	3	14.0	...	Volksparkstadion	50000.0	Bastian Dankert	https://www.transfermarkt.co.uk/hamburger-sv_1...	4-2-3-1	4-4-2 Diamond	Hamburger SV	1. Fußball- und Sportverein Mainz 05	2:3	domestic_league

5 rows × 23 columns

sporting_home = jogos[jogos['home_club_name'] == 'Sporting Clube de Portugal']
sporting_home.head()

	game_id	competition_id	season	round	date	home_club_id	away_club_id	home_club_goals	away_club_goals	home_club_position	...	stadium	attendance	referee	url	home_club_formation	away_club_formation	home_club_name	away_club_name	aggregate	competition_type
1398	2495298	CL	2014	Group G	2014-09-30	336	631	0	1	NaN	...	Estádio José Alvalade XXI	40734.0	Mateu Lahoz	https://www.transfermarkt.co.uk/spielbericht/i...	4-3-3 Attacking	4-2-3-1	Sporting Clube de Portugal	Chelsea Football Club	0:1	international_cup
1405	2526197	PO1	2014	26. Matchday	2015-03-22	336	2420	4	1	3.0	...	Estádio José Alvalade XXI	35979.0	Jorge Sousa	https://www.transfermarkt.co.uk/sporting-cp_vi...	4-3-3 Attacking	4-3-3 Defending	Sporting Clube de Portugal	Vitória Sport Clube	4:1	domestic_league
1419	2596940	PO1	2015	2. Matchday	2015-08-22	336	2995	1	1	5.0	...	Estádio José Alvalade XXI	40639.0	Manuel Oliveira	https://www.transfermarkt.co.uk/sporting-cp_fc...	4-4-2 double 6	4-2-3-1	Sporting Clube de Portugal	FC Paços de Ferreira	1:1	domestic_league
1442	2720784	PO1	2016	4. Matchday	2016-09-10	336	979	3	0	1.0	...	Estádio José Alvalade XXI	44393.0	Nuno Almeida	https://www.transfermarkt.co.uk/sporting-cp_mo...	4-4-2 double 6	4-3-3 Attacking	Sporting Clube de Portugal	Moreirense Futebol Clube	3:0	domestic_league
1456	2883759	PO1	2017	13. Matchday	2017-12-01	336	68608	1	0	2.0	...	Estádio José Alvalade XXI	46881.0	Nuno Almeida	https://www.transfermarkt.co.uk/sporting-cp_cf...	4-4-2 Diamond	4-4-2 double 6	Sporting Clube de Portugal	CF Os Belenenses	1:0	domestic_league

5 rows × 23 columns

plt.figure(figsize=(10, 6))
plt.plot(average_attendance_per_year, marker='o', linestyle='-', color='b')
plt.title('Average Stadium Attendance Per Year')
plt.xlabel('Year')
plt.ylabel('Average Attendance')
plt.grid(True)
plt.show()

Much can be said about this graph and why so much fluctuation in the average of assistance over the years.

Starting with the year 2012 and 2013, the average of assistance is relatively low because it coincides with Sporting's worst ranking ever in the Portuguese championship, they were seventh that year. The following year there is an increase because with the entry of a new president and some investment in the team allied with good results caused the average of assistance to increase, the same happened in 2015, although there was less assistance compared to the last year.

The 2016 boom is clearly influenced by the signing of Jorge Jesus the main rival's coach, good results, broke the record of points in the championship but still were not champions, finishing in second. The following year, champions league and good performance in the league kept the average of assists very high.

The fall in the following years is due to an attack that took place at Sporting's training centre, which caused many of the players to terminate their contract so that Sporting did not receive any value for many of the club's great assets. The investment was not the best, Sporting was in crisis, people didn't believe that Sporting wouln'd be good that year and it was reflected on assistance.

The year 2021 was the year in which Sporting won the championship again, but the games were played behind closed doors because of the pandemic.

Index

4.2 Players Market Value 2023

jogadores.head()

	player_id	first_name	last_name	name	last_season	current_club_id	player_code	country_of_birth	city_of_birth	country_of_citizenship	...	contract_expiration_date	agent_name	image_url	url	current_club_domestic_competition_id	current_club_name	market_value_in_eur	highest_market_value_in_eur	date	year
0	10	Miroslav	Klose	Miroslav Klose	2015	398	miroslav-klose	Poland	Opole	Germany	...	NaN	ASBW Sport Marketing	https://img.a.transfermarkt.technology/portrai...	https://www.transfermarkt.co.uk/miroslav-klose...	IT1	Società Sportiva Lazio S.p.A.	1000000.0	30000000.0	2012-08-05	2012
1	26	Roman	Weidenfeller	Roman Weidenfeller	2017	16	roman-weidenfeller	Germany	Diez	Germany	...	NaN	Neubauer 13 GmbH	https://img.a.transfermarkt.technology/portrai...	https://www.transfermarkt.co.uk/roman-weidenfe...	L1	Borussia Dortmund	750000.0	8000000.0	2012-08-05	2012
2	65	Dimitar	Berbatov	Dimitar Berbatov	2015	1091	dimitar-berbatov	Bulgaria	Blagoevgrad	Bulgaria	...	NaN	CSKA-AS-23 Ltd.	https://img.a.transfermarkt.technology/portrai...	https://www.transfermarkt.co.uk/dimitar-berbat...	GR1	Panthessalonikios Athlitikos Omilos Konstantin...	1000000.0	34500000.0	2012-08-05	2012
3	77	NaN	Lúcio	Lúcio	2012	506	lucio	Brazil	Brasília	Brazil	...	NaN	NaN	https://img.a.transfermarkt.technology/portrai...	https://www.transfermarkt.co.uk/lucio/profil/s...	IT1	Juventus Football Club	200000.0	24500000.0	2012-08-05	2012
4	80	Tom	Starke	Tom Starke	2017	27	tom-starke	East Germany (GDR)	Freital	Germany	...	NaN	IFM	https://img.a.transfermarkt.technology/portrai...	https://www.transfermarkt.co.uk/tom-starke/pro...	L1	FC Bayern München	100000.0	3000000.0	2012-08-05	2012

5 rows × 25 columns

jogadores['year'] = jogadores['date'].dt.year

sporting_players = jogadores[jogadores['current_club_name'] == 'Sporting Clube de Portugal']
sporting_players.head()

	player_id	first_name	last_name	name	last_season	current_club_id	player_code	country_of_birth	city_of_birth	country_of_citizenship	...	contract_expiration_date	agent_name	image_url	url	current_club_domestic_competition_id	current_club_name	market_value_in_eur	highest_market_value_in_eur	date	year
427	5404	Jérémy	Mathieu	Jérémy Mathieu	2019	336	jeremy-mathieu	France	Luxeuil-les-Bains	France	...	NaN	NaN	https://img.a.transfermarkt.technology/portrai...	https://www.transfermarkt.co.uk/jeremy-mathieu...	PO1	Sporting Clube de Portugal	1200000.0	15000000.0	2012-08-19	2012
2284	27303	NaN	Gerson Magrão	Gerson Magrão	2013	336	gerson-magrao	Brazil	Diadema	Brazil	...	NaN	GP Sports	https://img.a.transfermarkt.technology/portrai...	https://www.transfermarkt.co.uk/gerson-magrao/...	PO1	Sporting Clube de Portugal	200000.0	4000000.0	2012-11-25	2012
2289	27346	NaN	Marcelo Boeck	Marcelo Boeck	2015	336	marcelo-boeck	Brazil	Santa Cruz	Brazil	...	NaN	NaN	https://img.a.transfermarkt.technology/portrai...	https://www.transfermarkt.co.uk/marcelo-boeck/...	PO1	Sporting Clube de Portugal	200000.0	1800000.0	2012-11-25	2012
3375	39642	Bryan	Ruiz	Bryan Ruiz	2017	336	bryan-ruiz	Costa Rica	San José	Costa Rica	...	NaN	NaN	https://img.a.transfermarkt.technology/portrai...	https://www.transfermarkt.co.uk/bryan-ruiz/pro...	PO1	Sporting Clube de Portugal	150000.0	13000000.0	2013-04-21	2013
3743	44062	Antonio	Adán	Antonio Adán	2023	336	antonio-adan	Spain	Madrid	Spain	...	2024-06-30 00:00:00	Gesport Espizua SL	https://img.a.transfermarkt.technology/portrai...	https://www.transfermarkt.co.uk/antonio-adan/p...	PO1	Sporting Clube de Portugal	800000.0	8000000.0	2012-07-22	2012

5 rows × 25 columns

jogadores['last_season'] = pd.to_datetime(jogadores['last_season'], format='%Y')

jogadores['last_season'] = jogadores['last_season'].dt.year

sporting_players_2023 = sporting_players[sporting_players['last_season'] == 2023]
sporting_players_2023

	player_id	first_name	last_name	name	last_season	current_club_id	player_code	country_of_birth	city_of_birth	country_of_citizenship	...	contract_expiration_date	agent_name	image_url	url	current_club_domestic_competition_id	current_club_name	market_value_in_eur	highest_market_value_in_eur	date	year
3743	44062	Antonio	Adán	Antonio Adán	2023	336	antonio-adan	Spain	Madrid	Spain	...	2024-06-30 00:00:00	Gesport Espizua SL	https://img.a.transfermarkt.technology/portrai...	https://www.transfermarkt.co.uk/antonio-adan/p...	PO1	Sporting Clube de Portugal	800000.0	8000000.0	2012-07-22	2012
4819	56036	Luís	Neto	Luís Neto	2023	336	luis-neto	Portugal	Póvoa do Varzim	Portugal	...	2024-06-30 00:00:00	Team of Future	https://img.a.transfermarkt.technology/portrai...	https://www.transfermarkt.co.uk/luis-neto/prof...	PO1	Sporting Clube de Portugal	300000.0	6000000.0	2012-10-20	2012
7900	102427	Sebastián	Coates	Sebastián Coates	2023	336	sebastian-coates	Uruguay	Montevideo	Uruguay	...	2024-06-30 00:00:00	InterLex Sport	https://img.a.transfermarkt.technology/portrai...	https://www.transfermarkt.co.uk/sebastian-coat...	PO1	Sporting Clube de Portugal	4000000.0	15000000.0	2012-11-11	2012
9702	139395	Ricardo	Esgaio	Ricardo Esgaio	2023	336	ricardo-esgaio	Portugal	Nazaré	Portugal	...	2026-06-30 00:00:00	Proeleven S.A.	https://img.a.transfermarkt.technology/portrai...	https://www.transfermarkt.co.uk/ricardo-esgaio...	PO1	Sporting Clube de Portugal	3500000.0	6000000.0	2013-03-09	2013
13063	211072	NaN	Paulinho	Paulinho	2023	336	paulinho	Portugal	Barcelos	Portugal	...	2026-06-30 00:00:00	NaN	https://img.a.transfermarkt.technology/portrai...	https://www.transfermarkt.co.uk/paulinho/profi...	PO1	Sporting Clube de Portugal	6500000.0	15000000.0	2013-05-16	2013
16488	288253	Jerry	St. Juste	Jerry St. Juste	2023	336	jerry-st-juste	Netherlands	Groningen	Netherlands	...	2026-06-30 00:00:00	St. Juste Management	https://img.a.transfermarkt.technology/portrai...	https://www.transfermarkt.co.uk/jerry-st-juste...	PO1	Sporting Clube de Portugal	8000000.0	16000000.0	2012-09-22	2012
16624	290391	Nuno	Santos	Nuno Santos	2023	336	nuno-santos	Portugal	Trofa	Portugal	...	2027-06-30 00:00:00	Jorge Pires	https://img.a.transfermarkt.technology/portrai...	https://www.transfermarkt.co.uk/nuno-santos/pr...	PO1	Sporting Clube de Portugal	12000000.0	14000000.0	2012-10-06	2012
17431	314367	Marcus	Edwards	Marcus Edwards	2023	336	marcus-edwards	England	London	England	...	2026-06-30 00:00:00	ARETÉ	https://img.a.transfermarkt.technology/portrai...	https://www.transfermarkt.co.uk/marcus-edwards...	PO1	Sporting Clube de Portugal	28000000.0	30000000.0	2012-11-24	2012
17888	325443	Viktor	Gyökeres	Viktor Gyökeres	2023	336	viktor-gyokeres	Sweden	Stockholm	Sweden	...	2028-06-30 00:00:00	NaN	https://img.a.transfermarkt.technology/portrai...	https://www.transfermarkt.co.uk/viktor-gyokere...	PO1	Sporting Clube de Portugal	45000000.0	45000000.0	2013-03-02	2013
20076	379249	NaN	Matheus Reis	Matheus Reis	2023	336	matheus-reis	Brazil	São João da Boa Vista	Brazil	...	2025-06-30 00:00:00	ADM Esporte	https://img.a.transfermarkt.technology/portrai...	https://www.transfermarkt.co.uk/matheus-reis/p...	PO1	Sporting Clube de Portugal	8000000.0	8000000.0	2013-04-03	2013
20969	400831	Rafael	Camacho	Rafael Camacho	2023	336	rafael-camacho	Portugal	Lisboa	Portugal	...	2024-06-30 00:00:00	NaN	https://img.a.transfermarkt.technology/portrai...	https://www.transfermarkt.co.uk/rafael-camacho...	PO1	Sporting Clube de Portugal	1500000.0	4000000.0	2012-08-26	2012
21317	412669	NaN	Trincão	Trincão	2023	336	trincao	Portugal	Viana do Castelo	Portugal	...	2026-06-30 00:00:00	Gestifute	https://img.a.transfermarkt.technology/portrai...	https://www.transfermarkt.co.uk/trincao/profil...	PO1	Sporting Clube de Portugal	10000000.0	30000000.0	2012-08-04	2012
21718	426620	Pedro	Gonçalves	Pedro Gonçalves	2023	336	pedro-goncalves	Portugal	Chaves	Portugal	...	2027-06-30 00:00:00	Jorge Pires	https://img.a.transfermarkt.technology/portrai...	https://www.transfermarkt.co.uk/pedro-goncalve...	PO1	Sporting Clube de Portugal	28000000.0	38000000.0	2012-09-23	2012
22571	461496	Morten	Hjulmand	Morten Hjulmand	2023	336	morten-hjulmand	Denmark	Kastrup	Denmark	...	2028-06-30 00:00:00	Boutique Transfers	https://img.a.transfermarkt.technology/portrai...	https://www.transfermarkt.co.uk/morten-hjulman...	PO1	Sporting Clube de Portugal	26000000.0	26000000.0	2012-11-17	2012
22614	461943	Daniel	Bragança	Daniel Bragança	2023	336	daniel-braganca	Portugal	Almeirim	Portugal	...	2027-06-30 00:00:00	Gestifute	https://img.a.transfermarkt.technology/portrai...	https://www.transfermarkt.co.uk/daniel-braganc...	PO1	Sporting Clube de Portugal	5000000.0	8000000.0	2012-11-11	2012
23352	492319	Franco	Israel	Franco Israel	2023	336	franco-israel	Uruguay	Nueva Helvecia	Uruguay	...	2027-06-30 00:00:00	NaN	https://img.a.transfermarkt.technology/portrai...	https://www.transfermarkt.co.uk/franco-israel/...	PO1	Sporting Clube de Portugal	2500000.0	2500000.0	2013-02-01	2013
23600	504073	Eduardo	Quaresma	Eduardo Quaresma	2023	336	eduardo-quaresma	Portugal	Barreiro	Portugal	...	2025-06-30 00:00:00	Proeleven S.A.	https://img.a.transfermarkt.technology/portrai...	https://www.transfermarkt.co.uk/eduardo-quares...	PO1	Sporting Clube de Portugal	2500000.0	7000000.0	2013-01-19	2013
24035	524315	Hidemasa	Morita	Hidemasa Morita	2023	336	hidemasa-morita	Japan	Takatsuki, Osaka	Japan	...	2026-06-30 00:00:00	HEROE	https://img.a.transfermarkt.technology/portrai...	https://www.transfermarkt.co.uk/hidemasa-morit...	PO1	Sporting Clube de Portugal	12000000.0	12000000.0	2013-04-12	2013
24692	549006	Gonçalo	Inácio	Gonçalo Inácio	2023	336	goncalo-inacio	Portugal	Almada	Portugal	...	2027-06-30 00:00:00	MRP.POSITIONUMBER	https://img.a.transfermarkt.technology/portrai...	https://www.transfermarkt.co.uk/goncalo-inacio...	PO1	Sporting Clube de Portugal	40000000.0	40000000.0	2013-03-30	2013
27229	670717	Dário	Essugo	Dário Essugo	2023	336	dario-essugo	Portugal	Lisboa	Portugal	...	2025-06-30 00:00:00	Promoesport	https://img.a.transfermarkt.technology/portrai...	https://www.transfermarkt.co.uk/dario-essugo/p...	PO1	Sporting Clube de Portugal	2000000.0	2000000.0	2012-11-10	2012
27708	701979	Geny	Catamo	Geny Catamo	2023	336	geny-catamo	Mozambique	Maputo	Mozambique	...	2028-06-30 00:00:00	Promoesport	https://img.a.transfermarkt.technology/portrai...	https://www.transfermarkt.co.uk/geny-catamo/pr...	PO1	Sporting Clube de Portugal	4000000.0	4000000.0	2013-01-23	2013
28125	726701	Rodrigo	Ribeiro	Rodrigo Ribeiro	2023	336	rodrigo-ribeiro	Portugal	Viana do Castelo	Portugal	...	2025-06-30 00:00:00	PCR Sports	https://img.a.transfermarkt.technology/portrai...	https://www.transfermarkt.co.uk/rodrigo-ribeir...	PO1	Sporting Clube de Portugal	3000000.0	3000000.0	2013-01-19	2013
29872	946883	Iván	Fresneda	Iván Fresneda	2023	336	ivan-fresneda	Spain	Madrid	Spain	...	2028-06-30 00:00:00	LIAN Sports Group	https://img.a.transfermarkt.technology/portrai...	https://www.transfermarkt.co.uk/ivan-fresneda/...	PO1	Sporting Clube de Portugal	13000000.0	15000000.0	2013-05-11	2013
29987	974506	NaN	Rafael	Rafael	2023	336	rafael	Brazil	Sete Lagoas	Brazil	...	2028-06-30 00:00:00	ADRIANO SPORTS	https://img.a.transfermarkt.technology/portrai...	https://www.transfermarkt.co.uk/rafael/profil/...	PO1	Sporting Clube de Portugal	375000.0	375000.0	2012-08-22	2012
29988	974982	Ousmane	Diomande	Ousmane Diomande	2023	336	ousmane-diomande	Cote d'Ivoire	Abidjan	Cote d'Ivoire	...	2027-06-30 00:00:00	NaN	https://img.a.transfermarkt.technology/portrai...	https://www.transfermarkt.co.uk/ousmane-dioman...	PO1	Sporting Clube de Portugal	40000000.0	40000000.0	2012-08-22	2012
30021	984439	João	Muniz	João Muniz	2023	336	joao-muniz	Portugal	Ribeira Brava	Portugal	...	2028-06-30 00:00:00	Proeleven S.A.	https://img.a.transfermarkt.technology/portrai...	https://www.transfermarkt.co.uk/joao-muniz/pro...	PO1	Sporting Clube de Portugal	500000.0	500000.0	2012-09-26	2012

26 rows × 25 columns

sporting_players_2023['market_value_in_million'] = sporting_players_2023['market_value_in_eur'] / 1e6
sporting_players_2023

/var/folders/d2/pwql6b_j2sjbv968h9tr70_h0000gn/T/ipykernel_60628/329111145.py:1: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

	player_id	first_name	last_name	name	last_season	current_club_id	player_code	country_of_birth	city_of_birth	country_of_citizenship	...	agent_name	image_url	url	current_club_domestic_competition_id	current_club_name	market_value_in_eur	highest_market_value_in_eur	date	year	market_value_in_million
3743	44062	Antonio	Adán	Antonio Adán	2023	336	antonio-adan	Spain	Madrid	Spain	...	Gesport Espizua SL	https://img.a.transfermarkt.technology/portrai...	https://www.transfermarkt.co.uk/antonio-adan/p...	PO1	Sporting Clube de Portugal	800000.0	8000000.0	2012-07-22	2012	0.800
4819	56036	Luís	Neto	Luís Neto	2023	336	luis-neto	Portugal	Póvoa do Varzim	Portugal	...	Team of Future	https://img.a.transfermarkt.technology/portrai...	https://www.transfermarkt.co.uk/luis-neto/prof...	PO1	Sporting Clube de Portugal	300000.0	6000000.0	2012-10-20	2012	0.300
7900	102427	Sebastián	Coates	Sebastián Coates	2023	336	sebastian-coates	Uruguay	Montevideo	Uruguay	...	InterLex Sport	https://img.a.transfermarkt.technology/portrai...	https://www.transfermarkt.co.uk/sebastian-coat...	PO1	Sporting Clube de Portugal	4000000.0	15000000.0	2012-11-11	2012	4.000
9702	139395	Ricardo	Esgaio	Ricardo Esgaio	2023	336	ricardo-esgaio	Portugal	Nazaré	Portugal	...	Proeleven S.A.	https://img.a.transfermarkt.technology/portrai...	https://www.transfermarkt.co.uk/ricardo-esgaio...	PO1	Sporting Clube de Portugal	3500000.0	6000000.0	2013-03-09	2013	3.500
13063	211072	NaN	Paulinho	Paulinho	2023	336	paulinho	Portugal	Barcelos	Portugal	...	NaN	https://img.a.transfermarkt.technology/portrai...	https://www.transfermarkt.co.uk/paulinho/profi...	PO1	Sporting Clube de Portugal	6500000.0	15000000.0	2013-05-16	2013	6.500
16488	288253	Jerry	St. Juste	Jerry St. Juste	2023	336	jerry-st-juste	Netherlands	Groningen	Netherlands	...	St. Juste Management	https://img.a.transfermarkt.technology/portrai...	https://www.transfermarkt.co.uk/jerry-st-juste...	PO1	Sporting Clube de Portugal	8000000.0	16000000.0	2012-09-22	2012	8.000
16624	290391	Nuno	Santos	Nuno Santos	2023	336	nuno-santos	Portugal	Trofa	Portugal	...	Jorge Pires	https://img.a.transfermarkt.technology/portrai...	https://www.transfermarkt.co.uk/nuno-santos/pr...	PO1	Sporting Clube de Portugal	12000000.0	14000000.0	2012-10-06	2012	12.000
17431	314367	Marcus	Edwards	Marcus Edwards	2023	336	marcus-edwards	England	London	England	...	ARETÉ	https://img.a.transfermarkt.technology/portrai...	https://www.transfermarkt.co.uk/marcus-edwards...	PO1	Sporting Clube de Portugal	28000000.0	30000000.0	2012-11-24	2012	28.000
17888	325443	Viktor	Gyökeres	Viktor Gyökeres	2023	336	viktor-gyokeres	Sweden	Stockholm	Sweden	...	NaN	https://img.a.transfermarkt.technology/portrai...	https://www.transfermarkt.co.uk/viktor-gyokere...	PO1	Sporting Clube de Portugal	45000000.0	45000000.0	2013-03-02	2013	45.000
20076	379249	NaN	Matheus Reis	Matheus Reis	2023	336	matheus-reis	Brazil	São João da Boa Vista	Brazil	...	ADM Esporte	https://img.a.transfermarkt.technology/portrai...	https://www.transfermarkt.co.uk/matheus-reis/p...	PO1	Sporting Clube de Portugal	8000000.0	8000000.0	2013-04-03	2013	8.000
20969	400831	Rafael	Camacho	Rafael Camacho	2023	336	rafael-camacho	Portugal	Lisboa	Portugal	...	NaN	https://img.a.transfermarkt.technology/portrai...	https://www.transfermarkt.co.uk/rafael-camacho...	PO1	Sporting Clube de Portugal	1500000.0	4000000.0	2012-08-26	2012	1.500
21317	412669	NaN	Trincão	Trincão	2023	336	trincao	Portugal	Viana do Castelo	Portugal	...	Gestifute	https://img.a.transfermarkt.technology/portrai...	https://www.transfermarkt.co.uk/trincao/profil...	PO1	Sporting Clube de Portugal	10000000.0	30000000.0	2012-08-04	2012	10.000
21718	426620	Pedro	Gonçalves	Pedro Gonçalves	2023	336	pedro-goncalves	Portugal	Chaves	Portugal	...	Jorge Pires	https://img.a.transfermarkt.technology/portrai...	https://www.transfermarkt.co.uk/pedro-goncalve...	PO1	Sporting Clube de Portugal	28000000.0	38000000.0	2012-09-23	2012	28.000
22571	461496	Morten	Hjulmand	Morten Hjulmand	2023	336	morten-hjulmand	Denmark	Kastrup	Denmark	...	Boutique Transfers	https://img.a.transfermarkt.technology/portrai...	https://www.transfermarkt.co.uk/morten-hjulman...	PO1	Sporting Clube de Portugal	26000000.0	26000000.0	2012-11-17	2012	26.000
22614	461943	Daniel	Bragança	Daniel Bragança	2023	336	daniel-braganca	Portugal	Almeirim	Portugal	...	Gestifute	https://img.a.transfermarkt.technology/portrai...	https://www.transfermarkt.co.uk/daniel-braganc...	PO1	Sporting Clube de Portugal	5000000.0	8000000.0	2012-11-11	2012	5.000
23352	492319	Franco	Israel	Franco Israel	2023	336	franco-israel	Uruguay	Nueva Helvecia	Uruguay	...	NaN	https://img.a.transfermarkt.technology/portrai...	https://www.transfermarkt.co.uk/franco-israel/...	PO1	Sporting Clube de Portugal	2500000.0	2500000.0	2013-02-01	2013	2.500
23600	504073	Eduardo	Quaresma	Eduardo Quaresma	2023	336	eduardo-quaresma	Portugal	Barreiro	Portugal	...	Proeleven S.A.	https://img.a.transfermarkt.technology/portrai...	https://www.transfermarkt.co.uk/eduardo-quares...	PO1	Sporting Clube de Portugal	2500000.0	7000000.0	2013-01-19	2013	2.500
24035	524315	Hidemasa	Morita	Hidemasa Morita	2023	336	hidemasa-morita	Japan	Takatsuki, Osaka	Japan	...	HEROE	https://img.a.transfermarkt.technology/portrai...	https://www.transfermarkt.co.uk/hidemasa-morit...	PO1	Sporting Clube de Portugal	12000000.0	12000000.0	2013-04-12	2013	12.000
24692	549006	Gonçalo	Inácio	Gonçalo Inácio	2023	336	goncalo-inacio	Portugal	Almada	Portugal	...	MRP.POSITIONUMBER	https://img.a.transfermarkt.technology/portrai...	https://www.transfermarkt.co.uk/goncalo-inacio...	PO1	Sporting Clube de Portugal	40000000.0	40000000.0	2013-03-30	2013	40.000
27229	670717	Dário	Essugo	Dário Essugo	2023	336	dario-essugo	Portugal	Lisboa	Portugal	...	Promoesport	https://img.a.transfermarkt.technology/portrai...	https://www.transfermarkt.co.uk/dario-essugo/p...	PO1	Sporting Clube de Portugal	2000000.0	2000000.0	2012-11-10	2012	2.000
27708	701979	Geny	Catamo	Geny Catamo	2023	336	geny-catamo	Mozambique	Maputo	Mozambique	...	Promoesport	https://img.a.transfermarkt.technology/portrai...	https://www.transfermarkt.co.uk/geny-catamo/pr...	PO1	Sporting Clube de Portugal	4000000.0	4000000.0	2013-01-23	2013	4.000
28125	726701	Rodrigo	Ribeiro	Rodrigo Ribeiro	2023	336	rodrigo-ribeiro	Portugal	Viana do Castelo	Portugal	...	PCR Sports	https://img.a.transfermarkt.technology/portrai...	https://www.transfermarkt.co.uk/rodrigo-ribeir...	PO1	Sporting Clube de Portugal	3000000.0	3000000.0	2013-01-19	2013	3.000
29872	946883	Iván	Fresneda	Iván Fresneda	2023	336	ivan-fresneda	Spain	Madrid	Spain	...	LIAN Sports Group	https://img.a.transfermarkt.technology/portrai...	https://www.transfermarkt.co.uk/ivan-fresneda/...	PO1	Sporting Clube de Portugal	13000000.0	15000000.0	2013-05-11	2013	13.000
29987	974506	NaN	Rafael	Rafael	2023	336	rafael	Brazil	Sete Lagoas	Brazil	...	ADRIANO SPORTS	https://img.a.transfermarkt.technology/portrai...	https://www.transfermarkt.co.uk/rafael/profil/...	PO1	Sporting Clube de Portugal	375000.0	375000.0	2012-08-22	2012	0.375
29988	974982	Ousmane	Diomande	Ousmane Diomande	2023	336	ousmane-diomande	Cote d'Ivoire	Abidjan	Cote d'Ivoire	...	NaN	https://img.a.transfermarkt.technology/portrai...	https://www.transfermarkt.co.uk/ousmane-dioman...	PO1	Sporting Clube de Portugal	40000000.0	40000000.0	2012-08-22	2012	40.000
30021	984439	João	Muniz	João Muniz	2023	336	joao-muniz	Portugal	Ribeira Brava	Portugal	...	Proeleven S.A.	https://img.a.transfermarkt.technology/portrai...	https://www.transfermarkt.co.uk/joao-muniz/pro...	PO1	Sporting Clube de Portugal	500000.0	500000.0	2012-09-26	2012	0.500

26 rows × 26 columns

jogadores_sorted = sporting_players_2023.sort_values(by='market_value_in_million', ascending=False).head(10)

# Creating graph
plt.figure(figsize=(10, 6))
plt.bar(jogadores_sorted['name'], jogadores_sorted['market_value_in_million'], color='green')
plt.xlabel('Player')
plt.ylabel('Market Value (EUR)')
plt.title('Top 10 Market Value Sporting Clube de Portugal Players (23/24)')
plt.xticks(rotation=45, ha='right')
plt.tight_layout()
plt.show()

In the graph we can see that Viktor Gyokeres is the most 'valuable' player of Sporting followed by Goncalo Igacio and Diomande. Gyokeres is the highest purchase of Sporting of all time and his performance during this season makes him the most valuable player of Sporting at the moment.

Of this top 3 only Gyokeres plays in the attack, Inacio and Diomande play as defenders.

Index

5. Conclusion

The purpose of our analyse was to verify:

• Who are the 20 players that scored more goals

• Who are the 20 players that assisted more times

• Who has more contribution to goals

• Who are the 20 players that need less time to score a goal

• Who are the 20 players that need less time to assist someone to score

• Top 20 players that got more yellow cards

• Top 20 players that got more red cards

• Top 20 players with more card combination.

I started by importing the libraries, I found that there was a lot of missing data in some tables but as this data would not interfere with my study I decided not to change anything.

After changing some data, I started by doing my analyses.

• I found that Lewandowski was the player with the highest number of goals (maybe because our database did not have records of the Arab league and MLS, which certainly influenced the study).

  • Lewandowski - 453 Goals
  • Messi - 443 Goals
  • Ronaldo - 432 Goals

Note( Lewandowski has a total of 46014min, Messi 43543min and Ronaldo 41150min)

• Messi was the player with the most assists, followed by De Bruyne and Muller

  • Messi - 219 Assistants
  • De Bruyne - 195 Assistants
  • Muller - 190 Assistants

Note( Messi has a total of 43543min, De Bruyne 35054min and Muller 34384min)

The number of minutes played certainly affects the data a lot.

• Messi, Lewandowski and Ronaldo were the players who contributed the most to the goal:

  • Messi - 662 (443G/219A)
  • Lewandowski - 564(453G/111A)
  • Ronaldo - 546(432G/114A)

• Ronaldo, Messi and Mbappe are the players who need less time to score a goal:

  • Ronaldo - 95,26min
  • Messi - 98.29min
  • Mbappe - 101.56

• De Bruyne, Muller and Neymar Jr. are the players who need the least time to make an assist:

  • De Bruyne - 179.76min
  • Muller - 180.97min
  • Neymar Jr. - 198.35min

• Speaking of contribution per minute, Messi, Neymar and Kylian Mbappe are the players who need fewer minutes on average to contribute to goal (goal/assist):

  • Messi - 297.12min
  • Neymar Jr. - 332.14min
  • Mbappe - 340.27min

In a second part, I studied which players had the most yellow, red and total cards.

• I noticed that Danilo, Paulinho and Marcelo, all Brazilian players, saw more yellow cards.They're also top 3 of on sum of yellow and red cards.

• Marcelo, Felipe and Diagne were the players who saw the most red cards.

I filtered the 3 players that contributed the most for goals and assists over the years. Lewandowski, Messi and Ronaldo were the top 3 players that contributed the most, so I wanted to draw a graph that could show how they've done it over the years. I could see that in the first half of the years, records were broken by Ronaldo and Messi but over the years Lewandowski started to take the lead.

• The peaks are:

  • Messi tops with 78 contributions in the year 2016.
  • Ronaldo with 77 contributions in 2014.
  • Ronaldo with 76 contributions in 2013.

To finish my study, I spoke a little about Sporting, I saw that Sporting's stadium had the highest attendance in 2017 with an average of more than 40k people per game and a lowest attendance during Covid-19 pandemic where there were a lot of games without supporters in the stadium. Vyktor Gyokeres , Goncalo Inacio and Diomande are the players with highest value in the market at the moment.

Index